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Nsf final
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Nsf final

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  • \n
  • William Parsons, 3rd Earl of Rosse\n
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  • Ask the simplest useful question. \nKevin - and limitations of single expert classification. \n
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  • Working at web scale...or not. \nJump forward : solving this through judicious use of commercial technologies. \n
  • User weighting after the fact\nConfidence as well as answer\n
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  • Serendipitous discovery\n
  • Serendipitous discovery\n
  • What else can we do with a forum?\n
  • Collaboration.\n\nFace on S over E...SE...SS...’Q’...’X’...‘Phi\n
  • Object-orientated commenting. \n\nDifferent levels of promise - commitment to use 1 (&2?). \nExample of generalised problem. \n
  • Object-orientated commenting. \n\nDifferent levels of promise - commitment to use 1 (&2?). \nExample of generalised problem. \n
  • Object-orientated commenting. \n\nDifferent levels of promise - commitment to use 1 (&2?). \nExample of generalised problem. \n
  • Object-orientated commenting. \n\nDifferent levels of promise - commitment to use 1 (&2?). \nExample of generalised problem. \n
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  • Noon air temperatures. \n
  • HMS Africa : Ships crew ~600\nWeather observations throughout\n
  • \n
  • Very genralizable domain model\n
  • - Code reuse (allows small projects, media independence)\n- Keeping volunteers in one place : Good for us, progression for them\n- A quality assured set of projects. \n- Open calls, more projects to come\n\n\n
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  • April–July 2010, during which nearly 14,000 supernova candidates from PTF were classified by more than 2,500 individuals within a few hours of data collection. We compare the transients selected by the citizen scientists to those identified by experienced PTF scanners, and find the agreement to be remarkable – Galaxy Zoo Supernovae performs comparably to the PTF scanners, and identified as transients 93% of the ∼ 130 spectroscopically confirmed\n
  • April–July 2010, during which nearly 14,000 supernova candidates from PTF were classified by more than 2,500 individuals within a few hours of data collection. We compare the transients selected by the citizen scientists to those identified by experienced PTF scanners, and find the agreement to be remarkable – Galaxy Zoo Supernovae performs comparably to the PTF scanners, and identified as transients 93% of the ∼ 130 spectroscopically confirmed\n
  • April–July 2010, during which nearly 14,000 supernova candidates from PTF were classified by more than 2,500 individuals within a few hours of data collection. We compare the transients selected by the citizen scientists to those identified by experienced PTF scanners, and find the agreement to be remarkable – Galaxy Zoo Supernovae performs comparably to the PTF scanners, and identified as transients 93% of the ∼ 130 spectroscopically confirmed\n
  • Hubble time is rare - minimize false positives as best you can to get best hit rate. \nGive up on completeness, miss unusual examples. \n
  • Over the past 8 months, a community of more than 40,000 volunteers has classified less than 1% of the first LRO data release. With the LRO mission expected to last for at least another 7 years producing tens of terabytes of data with every release,\n\nLunar scientists are already experiencing the ‘data flood’, receiving unprecedented quantities of data from the Lunar Reconnaissance Orbiter (LRO). Sub-meter imaging of a significant fraction of the lunar surface is producing tens of terabytes of image data per year and attempts to take a similar approach to Galaxy Zoo with the Moon Zoo project (http://moonzoo.org) have demonstrated that this approach does not scale. So far, a community of over 40,000 volunteers has classified less than 1% of the first LRO data release. At current classification rates it will take over a century for the Moon Zoo community to analyze the full LRO dataset.\n\nHave a pipeline that is slicing and dicing but also registering the new ‘Assets’ with the Moon Zoo backend API.\nCatalogue size will depend upon mission lifetime - except that there is no ‘catalogue’\nSDSS for Galaxy Zoo\n
  • Over the past 8 months, a community of more than 40,000 volunteers has classified less than 1% of the first LRO data release. With the LRO mission expected to last for at least another 7 years producing tens of terabytes of data with every release,\n\nLunar scientists are already experiencing the ‘data flood’, receiving unprecedented quantities of data from the Lunar Reconnaissance Orbiter (LRO). Sub-meter imaging of a significant fraction of the lunar surface is producing tens of terabytes of image data per year and attempts to take a similar approach to Galaxy Zoo with the Moon Zoo project (http://moonzoo.org) have demonstrated that this approach does not scale. So far, a community of over 40,000 volunteers has classified less than 1% of the first LRO data release. At current classification rates it will take over a century for the Moon Zoo community to analyze the full LRO dataset.\n\nHave a pipeline that is slicing and dicing but also registering the new ‘Assets’ with the Moon Zoo backend API.\nCatalogue size will depend upon mission lifetime - except that there is no ‘catalogue’\nSDSS for Galaxy Zoo\n
  • Slicing and dicing images - moving target for the catalogue size\nProbably about 60,000,000 images - could be up to 600,000,000\n80GB / day raw images (30TB over first year)\nChanges the game for us a little\n
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  • Apprenticeship vs collective intelligence (apprenticeship is not scalable)\n
  • Data – 1. primary task data and 2. UBRET data (a) external data (b) user-generated data\n
  • Understanding human classification\n
  • The role of an intermediate interface\n
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  • These are all candidate planets that we found that had not been seen yet by Kepler. We just had a run on the Keck telescope last night\nTo follow up on some of these. \nAudio has a planet in it.\n
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  • Different scaffolding targeting different audiences\n
  • CI-TEAM is producing a tool for formal educators to share and adapt zoo-resources for the classroom; has group capability now.\n
  • State that these are wireframes; not designs.\n
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  • What motivates each class of volunteer?\nCredit JHU + Adler\n
  • eg Awareness : Initial - did they hear of the project, Sustained - Contribution is needed, Meta - identifies with the project.\nObligation Initial - absent, sustained - believes in project goals, meta - responsibility to project\n\nNext up - Giant Pilot Survey, targeted survey. \n
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  • Development strategy\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Citizen science as a facility\nNot open but nearly open\n
  • Inspired by Google Labs, but consistent site design and options.\n
  • App (now available in iPhone & Android) shows significant increase in number of classifications per user. iPhone - Mean ~1900 clicks per user! (Total > 3million)\nAlerts, social messaging. \n
  • Zooniverse home as a personalized, and updating portal.\n
  • Hubble time is rare - minimize false positives as best you can to get best hit rate. \nGive up on completeness, miss unusual examples. \n
  • Hubble time is rare - minimize false positives as best you can to get best hit rate. \nGive up on completeness, miss unusual examples. \n
  • Hubble time is rare - minimize false positives as best you can to get best hit rate. \nGive up on completeness, miss unusual examples. \n
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  • Transcript

    • 1. Citizen Science from Galaxy Zoo to the Zooniverse Chris Lintott Adler Planetarium; Oxford University Lucy Fortson University of Minnesota National Science Foundation May 10, 2011
    • 2. 70,000 60,000Classifications per hour 50,000 40,000 30,000 20,000 10,000 0 0 6 12 18 24 30 36 42 48 Hours
    • 3. Results from IRAM 30m BPT diagram classification Star Formation Transition Region AGN Schawinski, Lintott et al. 08, ApJ
    • 4. Results from IRAM 30m BPT diagram classification Star Formation Transition Region AGN Schawinski, Lintott et al. 08, ApJ
    • 5. Known knowns : Datareduction by science team
    • 6. Known knowns : Datareduction by science teamKnown unknowns : Resultsfunnelled to specific researchers
    • 7. Known knowns : Datareduction by science teamKnown unknowns : Resultsfunnelled to specific researchersUnknown unknowns : Discussiontool allows interesting objects tobecome prominent.
    • 8. Task Favourites PagesAnswer Annotation User Alerts Sections Asset Project Tags
    • 9. www.zooniverse.org
    • 10. !
    • 11. MOON
ZOO
    • 12. OLD
WEATHERMOON
ZOO
    • 13. MILKY
WAY
PROJECT &
PLANET
HUNTERS OLD
WEATHERMOON
ZOO
    • 14. Data from external sources (eg telescope) Primary Interface Output Data ScienceTraining sets (papers,for dataMachine release)
    • 15. Data from external sources (eg telescope) Advanced Tools 1. Discussion tools & fora 2. Advanced tools for data analysis 3. A ‘journal’ for citizen scientists Primary Interface Output Data ScienceTraining sets (papers,for dataMachine release)
    • 16. Data from external sources (eg telescope) Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums Output Data Science Training sets (papers, for data Machine release)
    • 17. Data from external sources (eg telescope) Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums Output Data Science Training sets (papers, for data Machine release)
    • 18. Data from external sources (eg telescope) Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums Education Research - What motivates volunteers to contribute at different levels? - Who is participating at which level? Output Data - What do they learn by doing so? Science Training sets (papers, for data Machine release)
    • 19. Data from external sources (eg telescope) Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums Education Research - What motivates volunteers to contribute at different levels? - Who is participating at which level? Output Data - What do they learn by doing so? Science Informed Training sets (papers, for Design & data Best Machine release) Practice
    • 20. Data from external sources (eg telescope) Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums Education Research CI-TEAM - What motivates volunteers to contribute at different levels? - Who is participating at which level? Output Data - What do they learn by doing so? Science Informed Training sets (papers, for Design & data Best Machine release) Practice
    • 21. Data from external sources (eg telescope) Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums ISE Education Research CI-TEAM - What motivates volunteers to contribute at different levels? - Who is participating at which level? Output Data - What do they learn by doing so? Science Informed Training sets (papers, for Design & data Best Machine release) Practice
    • 22. Data from external sources (eg telescope) CDI Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums ISE Education Research CI-TEAM - What motivates volunteers to contribute at different levels? - Who is participating at which level? Output Data - What do they learn by doing so? Science Informed Training sets for CDI Machine Learning (papers, data Design & Best release) Practice
    • 23. Cyber-enabled Discovery & Innovation Conquering the data flood with a transformative partnership between citizen scientists and machines
    • 24. Citizen Scientists and Machines I: Filter Mode
    • 25. Citizen Scientists and Machines I: Filter Mode
    • 26. Citizen Scientists and Machines II:Training Sets for Improved Algorithms
    • 27. Citizen Scientists and Machines II:Training Sets for Improved Algorithms
    • 28. 60,000,000
    • 29. Machine Learning Challenge – Fall 2011Produce a crater finding algorithm that replicateshuman classifications – apply to rest of LRO data
    • 30. Oxyrhynchus Papyri (!) 12
    • 31. Human/Machine partnership 13
    • 32. User-based Research Enabling Toolkits• Human element in data processing pipeline -> Serendipity -> user-based discoveries (Green Peas, variable stars)• Motivation study -> users want to participate in a real research project• Collective intelligence -> online peer mentorship -> “Talk” tool
    • 33. User-based Research Enabling Toolkits• Provide opportunity for users to explore data beyond the primary task• Tools that enable users to engage in the process of research from data -> papers
    • 34. Expert vs Non-expert Classifier (Thanks : John Radcliffe Hospital, Oxford)
    • 35. Outcome: MNRAS paper
    • 36. Citizen Scientist Tools 1. VisualizeData Archives 2. Search Literature 3. Analyse UBRETs Zoo Primary Task
    • 37. http://sky.astro.washington.edu:8081/ascot/cmddemo.html
    • 38. “Talk” =DiscussionTool-replacesthe forums
    • 39. Percentage of PlanetHunters users that visit Talk:  62.6%Percentage of Talk users that comment:  38.5%Percentage of PlanetHunters users that comment:  24.1%
    • 40. CI-TEAM Demonstration ProjectExpanding the Zooniverse : Refiningresources to support educator use of citizen science in the classroom.
    • 41. Current Work – Formal Education
    • 42. Current Work – Formal Education
    • 43. Informal Science EducationInvestigating audience engagement with citizen science.
    • 44. Types of volunteer• Initial volunteer – First-time user• Sustained volunteer – Continuing user• Meta volunteer – Works to enable contributions by other users
    • 45. • Crowston & Fagnot (2008) – Awareness: Do volunteers know their help is needed? – Capacity: Do they think they can do what is asked? – Obligation: Are they committed to continue? – Evaluation: Do they think benefits outweigh costs?• O’Brien & Toms (2010) – Volunteer-Website Interaction: How do they react to the website?• Model: five factors for three user types
    • 46. Data from external sources (eg telescope) CDI Advanced ToolsTools for morestructured learning 1. Discussion tools & fora 2. Advanced tools for data analysis- Class groups 3. A ‘journal’ for citizen scientists- teach.zooniverse.org for resource sharing- Alternative scaffolding Primary - For different ages Interface - For use in class - For museums ISE Education Research CI-TEAM - What motivates volunteers to contribute at different levels? - Who is participating at which level? Output Data - What do they learn by doing so? Training sets for Science Informed CDI Machine Learning (papers, data Design & Best release) Practice
    • 47. Project Domain Functionality requiredBodleian music Music/History Extendedwhale.fm Zoology transcription Audio classificationSETIQuest Astrophysics Live operationHurricane Climate science New drawing toolclassification ZooRadio Galaxy Astrophysics NoneRoman Britain Archeology Multiwavelength imaging GeneralizedOld Weather : Climate Science/Arctic History transcription Live operation/Protected Planet Environmental science multiwavelength imagingCrow Zoo Ecology NoneNEEMO/BioTagger Marine science GIS IntegrationGamma-ray Zoo Astrophysics None
    • 48. Science teampropose project
    • 49. Science teampropose project CSA Questionnaire
    • 50. Science teampropose project CSA Questionnaire What science will be produced? Why can’t the project be done automatically? What training will be necessary? What resources are available to exploit the results?
    • 51. Science teampropose project CSA Questionnaire Peer review
    • 52. Science teampropose project CSA Questionnaire Self-build Peer review Technical assessment Project Zooniverse Selection ‘Labs’
    • 53. Thanks to... The National Science Foundation The Leverhulme Trust, NASA, European Union FP7, SDSS, TED, Microsoft, Google, Scientific AmericanKaren Carney, Adam Tarnoff, Geza Gyuk, John Wallin, Kirk Borne, Jordan Raddick, Pamela Gay, JasonReed, Andrea Lardner, Michael Parrish, Arfon Smith, Rob Simpson, Stuart Lynn, William MacFarlane, JamesBrusuelas, the Zooniverse science teams and...
    • 54. Thanks to... The National Science Foundation The Leverhulme Trust, NASA, European Union FP7, SDSS, TED, Microsoft, Google, Scientific AmericanKaren Carney, Adam Tarnoff, Geza Gyuk, John Wallin, Kirk Borne, Jordan Raddick, Pamela Gay, JasonReed, Andrea Lardner, Michael Parrish, Arfon Smith, Rob Simpson, Stuart Lynn, William MacFarlane, JamesBrusuelas, the Zooniverse science teams and...Gordon Smith, Lucinda Donnachie, Philip Brohan, William, Donald Kindell, Helen Leggatt, Phil Gyford, Alex J, BaiLee, Justin Gallagher, Gustavo, Miclassicbuff, John Indus, Pierre Lindenbaum, Sam Elliott, Mark Adams, Simon Tokumine, Matt Francis, Matej Zavrsnik, Emma Gale, Peter Jan van Leeuwen, Thomas Blunsdon, PifPof,David Mear, Geoff Roynon, Ben Shepherd, Dougie, michaelforrest, Peter Dalling, Peter Clark, Paride Odierna, Sheila Bryant, Gaurav Vaidya, Robert Simpson, stuart Lynn, Francisco, Stuart Lowe, Edward Gomez, Marcin Janus, Ian Muller, Foe, Craig, Sergio Alvarez, Javier de la Torre, Steve N, Bexyboo, Emma, Jan Willem Tulp,Lori LaFrance, Emma Swadling, Rebecca, Wilko Graf von Hardenberg, Matt Lemieux, Ben Brumfield, Robert Eadie, kumsal_123, Richard Bevan, simon tanner, Dr Nicholas Hiley, Dr. Joseph Filakovsky, Aled Nicholas, Ron Diver, Ari Jokim√§ki, SJ, Matt Jones, Stefan Hauk, Jeremy Keith, David Swallow, Yayoi TSUTSUI, CodyStoltman, Steve Thompson, J Johnson, Lorraine Kelly, Henryk Krawczyk, Ian Molloy, lajos, patrick charles, carol, gautam, Dave Hollinberger, Torbjoern Klatt, stavros, Neil McPherson, Gerry Holmes, Arthur, Joe Fuller, K.Uyanik, jane harvell, Joseph Hughes, Vuk, Jen Southern, Kathryn Yeo, Rebecca, Bruce Shakespeare, TaviAnne Greiner, Article Dan, Xian, Pierrette Thomet, embersfire, Lily Lau WW, Steven Bamford, Svanhildur Bogadottir, Steve Crothers, Eric, Kevin, Dan Cramer, Emily, Ben Bodien, Christian Stewart, James Darling, Mark Couvillion, J√©r√¥me Danthinne, Kieron, Richard Keyzor, Slavin, Ewan Stevenson, Kevin Ashley, Jon Linklater-Johnson, Jane Stevenson, Peter Brohan, Rebecca Obrecht, Andrew Hayward, Paul Downey, Mel Palmer, Derek Law, Derek Bauman, Christian Beer, Anthony Moore, Stephen Lee, Sietse de Ruiter, Greg Lavallee, Matt Broadhead, Ian Hopkinson, James Wakelin, Scott, Kass Schmitt, Jonas Carlsson, Evan Caldwell, Bill Greer, DanEastwell, Damien McMahon, Wolf, Peter Mahnke, Scott Elfstrom, allister, Mike Smith, Steve, Chris Cohen, Ted Judah, Tim Print, Steve Wagner, Jonathan Gilbert, Sarah Fenn, Amy Forestell, Hannah Hutchins, Ian Eiloart, Cote, chris, Ryan Ricketts, Dr Steve Fleischer, Jared, Jamie Matthews, Christopher W. Shubert, Sean Gillies,Haley Gomez, Jane Richards, Nicola Twilley, Alex Lee, Brad, Jim Hill, Marc George, Luke, Mr. John Sly, samuel lanckmans, Deyan Tanev, Marten Veldthuis, Capt.Renjith V S, Hristo Iliev, Jeni Tennison, J. Demers, Kai Conragan, Csaba T√ºrk√∂si, Lee, Gareth Francis, Adrian Lillywhite, Krzysztof Kulanek, Kevin Marks, MassimoMezzoprete, Toto Trib, Theresa Ball, Luke Opperman, rooster, Mark, Michael Dain, Mark Slater, Stephen Glendon, Carl Ferkinhoff, Saenger [SETI.Germany], Eli Bressert, Matt Brown, Tess, Richard Urban, Michael Twidale, Lisa Grossman, Graham Pike, jinpa, Caro, Elisabeth Baeten, carlo, Matt Troutman, Simon Gilbert, PeterWoolliams, Rod McLaren, Suw Charman-Anderson, Matt Giguere, Barry Cairns, Rodney Pruitt, chris, Tyler Tillman, Robert Powell, Jean Squires, thatter, Louise Marston, dave, Tim Saunders, Tooting Bec, Jason Huber, Shackleford Hurtmore, bradley, javier bonnefont, Lauren Schenkman, vena, genevieve, Mike Fourcher, JonSteel, jonathan, Jakob Seerup, Tom Taylor, Ryan Schenk, Amanda Eversett, Jonathan, William Jackson, David Multer, Andrew Miller, sean, BW Diederich, Jessica, Jo Syan Pitkethly, Adam, Ben Marck, JuniorUK, Duncan de Young, Andrew Weyrauch, Tarehna, Curtis W. Garrett, Peter van Broekhoven, Craig Petrie, Michael Quinn,Anthony Casey, Darren Schmidt, M Tarpey, Tim Sherratt, hfrood, Nikki Kaylor, adam elbourne, OS, Al Anderson, Sam Margulies, Ben Emmel, Stephanie, Brian Ewins, Roland Schneider, Dan Connolly, Chris Pinthong, Jason Heiss, Kevin Burke, Joshua, mike, Joshua Douty, Wayne L, matthew chapman, Paul Thrasher, Caitlin,Ronnie, Brian Jones, Zack Ramadan, Frederic Laliberte, Jim Taylor :), Ephraim Bulow, Chris Boyer, Catherine, Matt Mollison, Jon Eben Field, Patricia Turner, Chris R, marianna, Jim, Rachel S., cmurtaugh, tyler, dan hodson, Ben Miller, JMDK2000, bobby rullo, Christopher L. Burdett, Brian Meents, peter johnstone, JonathanLepoff, Ed Gibson, Jonathan Pritchard, Drew, Daniel Bainbridge, Craig, Jeff Ray, Lauren, Juan Arredondo, jm, Padraig Murphy, Brad K., Dianna Leighton, Daniel Bagel, Jessamyn Donovan, Aimee Betker, neil keown, River Jiang, steve van tuyl, general john, Chris Rochford, megamani, Richard Newton, Scott Jenkins, Simon Tout,Katie Marshall, qwertyuiop67, Jeremy, Martin Hohenberg, Rob Mellor, raphael1, Jos√© S√°, Karl Bernst√•l, Dirk Toms, Erik, Daniel Clay, Ben S, Luke Weston, Bruce H., Anne Zarinnia, nataly hernandez, Igor, John Kind, Bulut Mehmet Yƒ±kƒ±lmaz, Francis James Beaver, Peter Hassack, Antonius Ipse, Peter Smith, JohnJohnson-Allen, terry malloy, Sara Garcia Hipolito, Peter Crooks, Ming, Donna W., Laura, Matthew Taylor, Ian Dew-Becker, Troy, Ele Munjeli, Greg Schivley, Edgar Dapremont, Eric Schliffka, Greg Long, Jeff Grooms, Matthew Cunningham, brandon smith, Andrew, David, Steve Bennett, Chap Lovejoy, Paul Pritchard, Marc Melcher,Chris Snee, Soren Scott, Dave Martin, Walter, Shireen Gonzaga, HSDonnelly, Penny Richards, James Ferguson, R Stenhouse, Bob, Nate Gay, Joe Panuto, Russell Neches, Isha Kuhns, John K, Dan Gordon, Colin Hynes, danceswithwords, Matt, David Black, Aladdin, Kate Walton, Clare Quinn, Mark Blake, Romana Challans, AlexWawro, alan cooper, Ross, Daniel R. 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