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h2. 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.



h2. 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.


h2. 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.


h2. 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.)



h2. Similarity measurements



In the following picture we summarize the different types of user information that we consider to provide different types of similarity measures:
!typesOfUserInfo.png|align=center,border=1,width=551,height=266!

h3. 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: !ProfileSimilarityFormula.png|align=center,width=385,height=57!
where _P_ is the set of properties of the user's profile, _b{_}{_}{~}p{~}_ is a parameter associated with each property that weights its importance, and _Similartiy{_}{_}{~}p{~}{_}_(u,x)_ is defined as follows:
!SimilarityPFormula.png|align=center,width=765,height=153!

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. 

h3. 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).

h3. 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:
!InteractionSimilarityFormula.png|align=center,width=384,height=94!
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.

  !FGFormula.png|align=center,width=242,height=73!
* _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:
*   !MFGFormula.png|align=center,width=609,height=86!
_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: !graphs.png|align=center,width=397,height=209!
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.

h2. 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)


h2. 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|Collaboration Spheres^OnBeingaScientist.pdf]** *


*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|http://arxiv.org/], [ADS|http://adswww.harvard.edu/]) Discovery of scripts is possible via technical website forums (e.g. [Astrobetter|http://www.astrobetter.com], [ASCL |http://asterisk.apod.com/viewforum.php?f=35]) 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|http://simbad.u-strasbg.fr/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|http://vizier.u-strasbg.fr/viz-bin/VizieR] 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|http://labs.adsabs.harvard.edu/fulltext/]) 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



h2. 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.

!portal_mockup.png|border=1,width=575,height=277!


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)

h2. 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.

{code}
[{
        "item" : {            
            "name": "user0",
            "type" : "user",
            "uri": "http://www.myexample.org/user.xml?id=18",
            "inner": {
                "adjacencies" : [
                {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=18"}
                    ]
            },
            "intermediate": {
                "adjacencies" : [
                {
                    "linkedTo": "http://www.myexample.org/user.xml?id=2"},
                    {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=1"},
                    {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=3"}
                    ]
            },
            "outter": {
                "adjacencies" : [
                {
                    "linkedTo": "http://www.myexample.org/user.xml?id=1"},
                    {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=2"},
                    {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=3"}
                    ]
            }
             
        }
    },
{
        "item" : {            
            "name" : "RO2",
            "uri" : "http://www.myexample.org/workflow.xml?id=2",
            "type" : "RO",
            "inner": {
                "adjacencies" : [
                {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=2"}
                    ]
            },
            "intermediate": {
                "adjacencies" : [
                {
                    "linkedTo": "http://www.myexample.org/user.xml?id=18"},
                    {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=3"}
                    ]
            },
            "outter": {
                "adjacencies" : [
                {
                    "linkedTo": "http://www.myexample.org/user.xml?id=1"},
                    {
                    "linkedTo": "http://www.myexample.org/workflow.xml?id=3"}
                    ]
            }
             
        }
    }
 ]
{code}




h2. Some useful queries (examples)

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

{code}
SELECT DISTINCT ?user ?title
WHERE {
  ?a mebase:has-annotator ?user.
  ?a meannot:uses-tag ?tag.
?tag pref:title ?title}
ORDER BY ?user ?title

{code}

{code}
PREFIX sioc: <http://rdfs.org/sioc/ns#>
PREFIX mebase: <http://rdf.myexperiment.org/ontologies/base/>
SELECT ?user ?group WHERE{?group sioc:has_member ?user} order by ?user ?group
{code}

h2. 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.

h3. 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:

{code}
 {
    "list": {
        "name": "USERNAME",
        "type": "User",
        "uri": "http://www.myexperiment.org/users/XX",
        "owner": {
            "relation": {
                "linkedTo": "http://www.myexperiment.org/users/XX",
                "recent": "false",
                "direct": "false",
                "indirect": "false",
                "other": "false"
            }
        },
        "friends": {
            "relation": [
                {
                    "linkedTo": "http://www.myexperiment.org/users/1Y",
                    "recent": "false",
                    "direct": "true",
                    "indirect": "true",
                    "other": "false"
                },
                {
                    "linkedTo": "http://www.myexperiment.org/users/10Y",
                    "recent": "false",
                    "direct": "true",
                    "indirect": "true",
                    "other": "false"
                }
            ]
        },
        "ros": {
            "relation": [
                {
                    "linkedTo": "http://www.myexperiment.org/workflows/10Y",
                    "recent": "false",
                    "direct": "false",
                    "indirect": "true",
                    "other": "true"
                },
		{
                    "linkedTo": "http://www.myexperiment.org/workflows/97Y",
                    "recent": "false",
                    "direct": "false",
                    "indirect": "false",
                    "other": "true"
                }
            ]
        }
    }
}
{code}
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:&nbsp;[http://ia-wf4ever.isoco.com/Collab/rest/getCollab?userIni=|http://ia-wf4ever.isoco.com/Collab/rest/getCollab?userIni=]\[userID\]

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

h3. 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&nbsp;*where "inner" is the an array of users or ROs that are included in the inner sphere.

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

And and example of a real call to the service could be:&nbsp;[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|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]

h2. 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|http://force11.org/white_paper]






























h4. Other Links

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


























h2. References

(Akora et al., 2011) Akcora C., Carminati B., Ferrari E., (2011)&nbsp;_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&nbsp; and Maes, 1995) Shardanand, U. and Maes, P.: 1995, 'Social Information Filtering: Algorithms for Automating "Word of Mouth"'.&nbsp; In: CHI '95: Conference Proceedings on Human Factors in Computing Systems, Denver, CO, pp. 210-217.

h2. 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 :&nbsp;[Recommender+System|http://www.wf4ever-project.org/wiki/display/docs/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/\*|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/\*|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/\*|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/\*|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/\*|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/\*|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 | 1.0 |
* *Maria Paula Balcazar-Vargas*
|| User || Similarity Value ||
| Alan Williams | 1.0 |
| E3matheus | 1.0 |
* *Steve Crouch*
|| User || Similarity Value ||
| Wilbo | 1.0 |
* *James Eales*
|| User || Similarity Value ||
| Simon Jupp | 1.0 |
| Mustafa | 1.0 |
| Ferenc HORVATH | 1.0 |
| Philip Y. | 1.0 |
* *Trisha Adamus*
|| User || Similarity Value ||
| Alan Williams | 1.0 |
| Peter Li | 1.0 |
| Andrea Wiggins | 1.0 |
| AbuJarour | 1.0 |
* *Duncan Hull*
|| User || Similarity Value ||
| David Withers | 0.5 |
| Jun Zhao | 1.0 |
| Alan Williams | 1.0 |
| Antoon Goderis | 0.5 |
| Jean-Claude Bradley | 1.0 |
| Capemaster | 1.0 |
| Bbrunet | 1.0 |
| John Brookes | 0.5 |
| Sathish9 | 1.0 |
* *Ronny van Aerle*
|| User || Similarity Value ||
| Don Cruickshank | 0.5 |
| Esca | 0.5 |
* *Kieren Lythgow*
|| User || Similarity Value ||
| Capemaster | 1.0 |
* *Ravi*
|| User || Similarity Value ||
| Wei Tan | 1.0 |
| AbuJarour | 1.0 |
* *Cjmei7*
|| User || Similarity Value ||
| Hamish McWilliam | 0.5 |
* *Giovanni Dall'Olio*
|| User || Similarity Value ||
| Niall Haslam | 0.5 |
| Wassinki | 1.0 |
| Hamish McWilliam | 0.5 |
| Carol Lushbough | 0.5 |
* *Nick Malleson*
|| User || Similarity Value ||
| Chrisser | 1.0 |
* *Xiaoliang*
|| 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 |
| Nolanfyh | 1.0 |
| Kawther | 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 |
* *Franck Tanoh*
|| User || Similarity Value ||
| David Withers | 0.25 |
| Don Cruickshank | 0.3333333333333333 |
| Alan Williams | 1.0 |
| Paul Fisher | 1.0 |
| J. Seitz | 1.0 |
| George | 1.0 |
| Yergens | 1.0 |
| Andrew David King | 1.0 |
| Ian Laycock | 1.0 |
| E3matheus | 1.0 |
* *Rexissimus*
|| User || Similarity Value ||
| Mark Borkum | 0.2 |
| Paul Fisher | 0.5 |
| Jim Craig | 0.5 |
| David Lapointe | 0.5 |
| Peter Li | 0.5 |
| Andrew David King | 0.5 |
| Ian Laycock | 0.5 |
| AbuJarour | 0.5 |
* *Hong Chang Bum*
|| User || Similarity Value ||
| J. Dai Scientist | 1.0 | myexample |

SELECT DISTINCT ?user ?title
WHERE {
&nbsp;&nbsp;?a mebase:has-annotator ?user.
&nbsp;&nbsp;?a meannot:uses-tag ?tag.
?tag pref:title ?title}
ORDER BY ?user ?title