A Cabinet Of Web2.0 Scientific Curiosities


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A wander through trends in online science with a focus on how web2.0 may impact identity, reputation and citizen science.

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A Cabinet Of Web2.0 Scientific Curiosities

  1. 1. A Cabinet of Web 2.0 Scientific Curiosities Ian Mulvany, Product Development Manager, Nature Publishing Group This talk takes a tour through science related web 2.0 efforts and discusses areas of the practice of science that can be impacted through web 2.0 approaches. A video of this presentation will be posted at http://videolectures.net/
  2. 2. Some of the people involved • Timo Hannay - Director Nature.com • Jason Wilde - Publisher Physical Sciences • Amanda Ward - Head of Platform Technologies • Tony Hammond - Applications Architect • Alf Eaton - Product Development Manager • Euan Adie - Product Development Manager • Gavin Bell - Product Development Manager • Hilary Spencer - Product Development Manager • Ian Mulvany - Product Development Manager
  3. 3. • Publishing Industry Facts & Figures • Nature • (Some) Issues that Web 2.0 can impact • Identity and Authority • Content Discovery • Citizen Science • Google Wave • Ongoing Challenge • The Future
  4. 4. Publishing Industry facts & figures
  5. 5. Funding sources Source: Research Information Network
  6. 6. Costs of research Source: Research Information Network A significant contribution to the total cost of research is the time required for researchers to find the appropriate material for reading. There is an opportunity here to decrease such costs through creation of better tools for information discovery. source http://www.rin.ac.uk/
  7. 7. Nov 4, 1869
  8. 8. • "It is intended, first, to place before the general public the grand results of scientific work and scientific discovery" • "to aid scientific men ... by affording Norman Lockyer them an opportunity of discussing the various scientific questions that arise from time to time" Nature is principally a scientific communication company. We have to engage with the methods of communication that are important for science. If we started today our starting point would naturally be the web, and not a print journal.
  9. 9. (Some) Publishing Milestones • 1896, Wilhelm Röntgen, X-Rays • 1925, Raymond Dart , Australopithecus africanus • 1938, P Kapitza, Superfluidity • 1953, J D Watson and F H C Crick, DNA • 1985, J C Farman, B G Gardiner and J D Shanklin, Ozone Hole • 1995, Michel Mayor and Didier Queloz, Extra Solar Planets • 2001, Human Genome
  10. 10. Journal Evolution •1869 Journal Founded •1899 Journal Makes a Profit •1967 Peer Review •1971 First Expansion (until 1974) •1992 Nature Genetics •1995 Holzbrink Ownership •1995 Nature.com •2004 Connotea •2007 Nature Network Peer review only introduced in 1967 in order to deal with a backlog of about 3000 manuscripts.
  11. 11. Our current list of publications: http://www.nature.com/siteindex/index.html
  12. 12. Going beyond journals slide credit: Timo Hannay
  13. 13. 2.0 Web 2.0 is about getting and using data. There are two aspects, one is about lowering the barrier for participation, and the second is about data mining the resultant information in order to provide better services or tools. This can also lead to a strong first mover advantage, as the network of data or participation gets bigger the value in the network gets bigger
  14. 14. Web 1.0 Web 2.0 DoubleClick Google AdSense Ofoto Flickr Akamai BitTorrent mp3.com Napster Britannica Online Wikipedia personal websites blogging evite upcoming.org and EVDB domain name speculation search engine optimization page views cost per click screen scraping web services publishing participation CMS wikis directories (taxonomy) tagging (folksonomy) stickiness syndication
  15. 15. photo: flickr keso Google data mined the link structure of the web,
  16. 16. eBayʼs value comes from having such a large market. There is a buyer and seller for everything.
  17. 17. photo: flickr Dystopos Wall mart uses realtime data for logistics.
  18. 18. Data Tags, blogs, wikiʼs are incidental. They are tools for enabling the creation of more data.
  19. 19. image credit sam brown, explodingdog Should be aware not to focus on just the technology " " Building for Machines: " " " Semantic Markup " " " Well documented API's " " " " " Building for Humans:" " " " reduce the barrier to participation " " " increase the usefulness of serendipity and recommendation
  20. 20. Stay Classy, SXSW: Building Respectful Software http://panelpicker.sxsw.com/ideas/view/3691?return=%2Fideas%2Findex %2Finteractive%2Fq%3Abuilding+respectful make your software respectful http://panelpicker.sxsw.com/ideas/view/3691?return=%2Fideas%2Findex%2Finteractive%2Fq%3Abuilding+respectful
  21. 21. “ While scientists have gloried in the disruptive effect that the Web is having on publishers and libraries, with many fields strongly pushing open publication models, we are much more resistant to letting it be a disruptive force in the practice of our disciplines.” Jim Hendler Scientists resist Although the idea of a data driven approach should have an appeal to scientists, science changes slowly. There are a lot of implicit norms that are hard to change.
  22. 22. } NIH requests all Nature offers to fundholders upload to PubMed deposit their 70% of Central on behalf manuscripts in scientists can’t of authors with PubMed Central even be their permission archive bothered to say } } ”yes” 4% compliance 30% compliance Scientists resist An example of low participation in open data models is the low uptake of deposition of articles into pubmed.
  23. 23. Some Issues Where Web 2.0 May Help in Science • Identity and Reputation • Content Discovery • Citizen Science
  24. 24. Humans Public Academic Machines This is the framework that Iʼm going to be using to think about the topics in this talk. These are just two dimensions against which one can look at things, there are many other ways of looking at these issues. When putting together these slides I got interested in the tension between machine oriented efforts and human oriented efforts on the web. In addition web 2.0 can have a big impact on public engagement with science, so I wanted to see if I could line up these two trends together.
  25. 25. Who am I?
  26. 26. Identity on the web is a fractured thing. It makes it difficult to manage all of the accounts that a person has, but on the other hand it makes it easy to present different personas to different online communities.
  27. 27. 100, 000 Identity is a significant and growing issue in science. Each year India produces 100, 000 postdocs. Full names are often not revealed owing to caste discrimination. http://www.nature.com/nature/journal/v452/n7187/full/452530d.html
  28. 28. 1.1 Billion > 129 photo: Szymon Kochanski 129 surnames are shared by 1.1 billion people, 85% of the chinese population. Generally identity is a self enforcing protocol. Works most of the time, but ... Surgeon Liu Hui, padded his CV with publications by another researcher who shared his surname and initial, rose to become an assistant dean at Tsinghua University. Discrepancies were noticed and he was dismissed by the university in March 2006
  29. 29. http://www.mluvany.net Scopus Author ID 6603325879 Thompson Researcher ID B-2805-2008 CrossRef 62.1000/182 Contributor ID These are currently the most commonly discussed options for managing identity within an academic context, each has pros and cons, and none has gained enough momentum to be universally adopted. Nature is currently taking a wait and see approach, but we would like to see an open system gaining adoption.
  30. 30. Why is the issue of identity important, for reputation!
  31. 31. 1619 - 1677 Henry Oldenburg, first secretary of the Royal Society, invented the practice of peer review with the Transactions of the Philosophical Society. His own reputation suffered, he was jailed for being a potential dutch spy and thrown in the tower of london for a while.
  32. 32. TM Impact Factor IF (year) = A/B A = # of articles published in (year -1) + (year - 2) B = # of citations to journal in year
  33. 33. Impact factor measures an average statistic of a single journal. 80% of citations into a journal come from 20% of articles. General agreement that IF is a poor measure of individual article quality.
  34. 34. The citation network can be used to look at the relationship between journals.
  35. 35. doi/10.1371/journal.pone.0004803.g007 Other metrics can also reveal the connections between the sciences, Bollen et al. used website access data from publisherʼs http logs to look at how people browed the literature. This gave a more rounded picture than just looking at citations.
  36. 36. There is a move to now look instead of at journal level metrics rather
  37. 37. Citations time One thing that fascinates me about citations is that they are unidirectional. Also there must be more citations than papers, and yet 85% of papers receive at most 1 citation.
  38. 38. Ideas time They can be used to study the flow of ideas forward in time.
  39. 39. Main-path analysis and path-dependent transitions in HistCite™-based historiograms Journal of the American Society for Information Science and Technology (forthcoming) Diana Lucio-Arias1 & Loet Leydesdorff2 Amsterdam School of Communications Research (ASCoR), University of Amsterdam Kloveniersburgwal 48, 1012 CX Amsterdam, The Netherlands. This is the Main-Path Analysis technique, but as yet such analysis tends to be done on a case by case basis.
  40. 40. 1 Cox, D.R. (1972) Regression models and life-tables. J. Roy. Statist. Soc. B 34: 21 000 Some papers act as a kind of black hole for citations, they get into the literature and get cited and cited and cited. This paper has over 21 000 citations. The mis-citations to this paper have a h-index of 12, a level that Hirsch had concluded “…might be a typical value for advancement to tenure…” http://network.nature.com/people/boboh/blog/2008/06/24/outdone-by-mis-prints
  41. 41. Weaving in more value foto: flickr Naveen Roy
  42. 42. y easy plain text, emails hyperlinks Twitter views tags citations? contributing microformats MicroFormats (semantic web) academic papers Semantic Web hard mining easy PDF sucks, academic papers are hard to create and PDF is hard to extract any useful information from in a programatic way.
  43. 43. Humans Article Writing Peer Review Author Identification Article Publishing Public Academic Machines This is where most of the academic publishing workflow currently lives, it is manual work that can only be done by highly trained experts.
  44. 44. XML At nature we are consolidating all of our article content into a sigle XML database.
  45. 45. Building a delivery infrastructure http://www.flickr.com/photos/zhzheka/ We then deliver this content via print, RSS, paper, search queries, to a host of endpoints.
  46. 46. XML Blue - Done Green - Done within the last year Yellow - coming to completion Red - depreciated
  47. 47. Extensible Containers http://www.flickr.com/photos/cherieking/ We want to be able to extend the data that we deliver.
  48. 48. XML Medline + MESH We pull in MESH terms for our articles from medline post-publication.
  49. 49. Case Study: Nature Chemistry We have started extracting entities from our Nature Chemistry journal, and we hope to roll this program out to other journals.
  50. 50. HO CAS – 50-67-9 NH 2 NH Serotonin SMILES – Oc1cc2c(cc1)ncc2CCN InChI – 1S/C10H12N2O/c11-43-7-6-12-10-2-1-8(13)5-9 (7)10/h1-2,5-6,1 2-13H,3-4,11H2 InChIKey – QZAYGJVTTNCV MB-HFFFAOYSA-N Chemistry is a visual science! molecules cas #s first appeard in 1907, is owned by ACS, contains no semantics smiles 1987, not unique to a compound Inchi/Inchikey 200/2005
  51. 51. GIF/PNG GIF/PNG 3D Author file Author file Compound Data CDX CDX
  52. 52. Enhanced compound pages offer: Chemdraw file CML file View structure in 3D Synonyms Chemical formula Molecular Weight Elemental Analysis InChI and InChIKey SMILES string Links to external databases
  53. 53. PubChem InChi ChemSpider We can start to link from articles into databases, and vice versa.
  54. 54. PubChem ChemSpider XML TXT xpath Medline UIMA + MESH Schematic of our current entity extraction workflow, Initially we are extracting chemical and compound names form Nature Chemistry articles.
  55. 55. We have a bespoke interface that allows editorial curation of the annotations.
  56. 56. <dl class="meta"> <dt>InChI</dt> <dd class="inchi">InChI=1/ C10H14N5O7P.2Na/c11-8-5-9 (13-2-12-8)15(3-14-5)</dd> </dl> Making the markup of the bold numbers makes the online version of the paper more semantic,
  57. 57. Organise metadata: create good architecture so generated data can be easily reused across a range of applications. http://www.flickr.com/photos/timecollapse/ We hope to be able to extended the types of entities that we are extracting from our articles.
  58. 58. Expanding the annotation of journal articles from Nature Chemistry to Nature Chemical Biology and then to all NPG journals Creating a central NPG database of compounds and related journal articles
  59. 59. InChI=1S/C32H16N8.Cu/c1- N 2-10-18-17(9-1)25-33- 26(18)38-28-21-13-5-6-14-22 N N (21)30(35-28)40-32-24-16-8- N Cu N 7-15-23(24)31(36-32)39-29- N N 20-12-4-3-11-19(20) 27(34-29)37-25;/h1-16H; N This then makes the article a more integrated object, with links to databases, entities and the products of scientific research.
  60. 60. There are many curated databases that look for information about domain specific results in the literature. An example is flybase that collects information about results using the model organism Drosophila.
  61. 61. Wormbase does the same for C. elegans. Both require a large amount of human curating. Having the body of scientific literature be semantically annotated should help with this kind of curation.
  62. 62. Site such as Chemspider and Crystal Eye demonstrate what can be done though data mining the literature.
  63. 63. So we have moved into a situation in which our scholarly network can now connect to entity databases, rather than just to articles.
  64. 64. Humans Article Writing Peer Review Author Identification Article Publishing Public Academic Entity Extraction Machines Article publishing hopefully becomes enriched through semantic markup and entity extraction.
  65. 65. Getting Social photo credit: flickr mcgeez We can go beyond published articles and entities and look at both other published artefacts and the social annotation that is associated with them.
  66. 66. The amount of grey literature available in physics has grown steadily, as displayed by submissions to the Physics ArXiVe.
  67. 67. Nature Precedings was the first preprint server for the life sciences. It also includes the ability to vote and comment on submissions and provides each submission with a unique identifier.
  68. 68. PLoS have launched PloS Currents: Influenza, based on top of Google Knol. Both Preceedings and Currents have editorial curation of content, and allow easy publication of objects such as posters, proceedings papers and white papers.
  69. 69. Connotea is Natureʼs social bookmarking service for academics.
  70. 70. It can extract citation information form a range of online resources, saving the author the effort of manually entering this information.
  71. 71. Title Author Date PMID/DOI Tags
  72. 72. The Kind of Information that we can capture with Connotea includes full citation information Usage patterns, (when did an item get added to our DB, how many times has it been added) Extra meta-data such as tags Potentially social network information, how many of my friends have added this item?
  73. 73. Total number of tags Total number of unique tags Growth in usage of the service has been steady
  74. 74. And it displays the characteristic power law behaviour of an online network.
  75. 75. 11032
  76. 76. It supports data export via txt, rdf, BibTex, RIS, and EndNote
  77. 77. RDF HTML RSS TXT
  78. 78. http://www.connotea.org/user/IanMulvany http://www.connotea.org/users/tag/scifoo http://www.connotea.org/user/IanMulvany/tag/ scifoo http://www.connotea.org/user/IanMulvany/tag/science http://www.connotea.org/user/IanMulvany/tag/ science2.0+citation Example of calls to query the data, html output
  79. 79. http://www.connotea.org/data/user/IanMulvany http://www.connotea.org/data/users/tag/scifoo http://www.connotea.org/data/user/IanMulvany/tag/ scifoo http://www.connotea.org/data/user/IanMulvany/tag/ science http://www.connotea.org/data/user/IanMulvany/tag/ science2.0+citation Example of API calls
  80. 80. There are plenty of other such services currently available. Interestingly Fuzzy has the most semantically enabled technology, but is one of the least used.
  81. 81. A few start-ups are redefining the academic paper management space, Papers is a mac based “iTunes” for Pdfs.
  82. 82. Mendeley provides the same kind of features, with a Last FM metadata scrobbling model.
  83. 83. This allows one to see data on what is being read in Mendeley libraries. This starts to open up a new layer of information about the impact of papers that goes beyond what can be captured by the impact factor.
  84. 84. Nature Network Online social communities also allow us to begin to capture conversations about science. NPG launched Nature Network in 2009 and is one of the most active online forums for the discussion of science.
  85. 85. It has specific features to allow members to track the conversations that they have participated in.
  86. 86. There are 3 main local hubs, but we track the geographic location of members, and try to connect people with other members in their neighbourhood.
  87. 87. Bringing things together photo: flickr Thomas Hawk Q: How do you manage all of these streams of information? A: Aggregation is one answer (probably not the only answer).
  88. 88. PostGenomic aggreagtes science blogs and picks out popular items.
  89. 89. Nature blogs finds blog posts that discuss scientific articles. Science Blogs and researchblogging.org do much the same.
  90. 90. Scinitalla is another Nature product that creates recommendations based on a users reading habits.
  91. 91. Friend Feed aggregates discussions around resources from difference sources. It has seen widespread adoption by the scientific digerati, the life scientists room is one of the most active.
  92. 92. People are using these rooms to have real-time conversations around real-time events. This broadcasts an event and the conversions around an event to the web. It enables real time distant participation.
  93. 93. streamosphere.nature.com/preview.php is an aggregator for discussions on twitter, friendfeed some other lightweight user signals. It again aggregates over a curated list of sources.
  94. 94. So now we can see a world in which the article is no longer the only digital artefact of note. Much more of the process of science is becoming visible through online engagement of scientists.
  95. 95. Humans Article Writing Peer Review Author Identification Article Publishing Science Blogging/Tweeting/Social Communities SIOC Public Academic Entity Extraction Machines Social media as it exists now is problematic - effervescent - closed - siloed - unstructured Tools like SioC, an ontology for social media, can help draw this layer of information to the machine.
  96. 96. Citizen Science
  97. 97. Seti@home Folding@home “Thinking@home” One kind of participatory science is getting users to donate their hardware.
  98. 98. 10 000 sheep, Aaron Koblin, 2006 You can also build interfaces to people, e.g. the Mechanical Turk. The sheep market created by Aaron Koblin in 2006 by getting 10 000 turks to draw sheep.
  99. 99. + = Cheap Sentiment Analysis
  100. 100. http://blog.doloreslabs.com/2009/05/the-programming- language-with-the-happiest-users/ Two people checking a subset of tweets can data mine twitter for you. We used crowdsourcing to analyse all of the comments to PlOS articles.
  101. 101. But another more interesting version is to get people in interact directly with your data! " stardust at home " http://stardustathome.ssl.berkeley.edu/about.php " http://folding.stanford.edu/ " http://fold.it/portal/ " citizen science blog " http://citizensci.com/ " great backyard bird count " http://www.birdsource.org/gbbc/
  102. 102. You need to make it engaging, like the Fold it Project, or Galaxy Zoo. Even if machines and machine learning could answer some of these questions (like image analysis of galaxy rotation), humans can do it now. You get the scientific benefit now, you engage the public with science now.
  103. 103. Fold it Stardust at home Humans Article Writing Peer to Patent Peer Review Galaxy Zoo Author Identification Article Publishing Science Blogging/Tweeting/Social Communities Turk SIOC RDF Public Academic Entity Extraction Seti at Home Folding at home Machines Now we have an interesting picture, but most of the arrows in this picture point down. Where are the efforts to make computers more friendly to people? One pointer to how that will happen in the future is Google Wave.
  104. 104. Google Wave photo credit: flickr prgibbs New product from Google, launching in September 09 For the definitive guide to google wave look at: http://www.youtube.com/watch?v=v_UyVmITiYQ
  105. 105. wave Currently there is a lot of hype, and not much access to the product.
  106. 106. Robot App Engine Gadget html5 Embed Container (blogger) Of interest for developers are the APIʼs the wave exposes. Naively one can think of Robots as allowing two way communication with a wave, Gadgets for pulling content into a wave, and the Embed gadget as a tool for pushing waves into other contexts, such as blogs or wikis.
  107. 107. Importantly Google intends to open source the server code allowing anyone to run a wave server, much as anyone can run an email server.
  108. 108. Email Thread? Document? Game Server? IM? Gallery? Group? ? ?? ? The metaphors for what wave is have not settled down yet. This is a consequence of the current interface, new interfaces will be possible. The key is that Wave enables exposing 3rd party APIʼs to the user in a totally opaque way. It hides the details, and makes it easier for people to interact with computers.
  109. 109. image credit sam brown, explodingdog Finally we can live in a a world where computers and humans can be friends.
  110. 110. Fold it Stardust at home Humans Article Writing Peer to Patent Peer Review Galaxy Zoo Author Identification Article Publishing Science Blogging/Tweeting/Social Communities Turk SIOC RDF Public Academic WAVE Entity Extraction Seti at Home Folding at home Machines
  111. 111. • http://code.google.com/p/helpmeigor/ • http://github.com/cameronneylon/ChemSpidey/ tree/master • http://github.com/IanMulvany/janey-robot/tree/ master Some scientific robots have already been created.
  112. 112. Visualisation photo credit: flickr mrcthepc Insight requires good visualisation techniques.
  113. 113. Eigenfactor.org An example of great visualisation of the relationships between journals.
  114. 114. Stamen.com have created some of the best data visualisations on the web.
  115. 115. Map tube is an interesting project allowing the mashup of geo-data.
  116. 116. http://iphylo.blogspot.com/2009/08/mammal-tree-from-wikipedia.html Good visualisation provides insight, such as these visualisations of the phylogenic nodes present in wikipedia. Avian flu maps http://declanbutler.info/blog/?p=58 is another great example.
  117. 117. biological pathways Text http://www.reactome.org/ Itʼs a hard problem, some data sets are big and complicated. http://www.reactome.org/ tries to visualise pathways in the human genome.
  118. 118. The Future Where are we heading to?
  119. 119. • Publishers will continue to exist but will become communication companies • They must learn to treat the web as a network, not a distribution channel • Journals should be more like databases, and vice versa • Publishing and broadcasting are merging (or colliding?); to some extent, he same goes for publishing and software • The disruptive forces include new economics, lower barriers to entry, and a complex competitive environment Final thoughts Some predictions for scientific publishing.
  120. 120. • Mobile devices as sensors e.g. noisetube.net • Rich web applications building on HTML 5 will be a real competitor to the desktop • The problem of scientific identity will be solved • We will have a scientific recommendation engine that works • Frameworks for programming genetic code, much like we now program computer code, will be available • Computers will do much of the heavy lifting of science • http://www.nature.com/nature/focus/arts/futures Final thoughts Some predictions for science.
  121. 121. “The future is already here. It's just not very evenly distributed” - William Gibson Sci Foo is an annual weekend un-conference that brings together people doing interesting things at the interface between science, technology and culture. Looking at what these people are doing gives us a hint of things to come.
  122. 122. http://www.nature.com/scifoo/index.html http://blogs.nature.com/wp/nascent/ http://www.connotea.org/user/IanMulvany/tag/active-ss @IanMulvany http://www.slideshare.net/IanMulvany
  123. 123. Extra image Acknowledgements • http://www.flickr.com/people/matthewfield/ Matthew Field, Lots Of People • http://www.flickr.com/people/garthimage/ Garth Burgess, Southampton Docks • http://13c4.wordpress.com/ Pamela Bumstead, 50 reasons not to • http://www.flickr.com/people/mayeve/ clock • http://www.flickr.com/people/sublimelyhappy/ Sarah Gerke, Rolodex • http://www.flickr.com/people/thedepartment/ Kate Andrews, Library • http://www.flickr.com/people/sirstick/ Alexander Hauser, new mail • http://commons.wikimedia.org/wiki/User:CJ The Thinker • Gavin Bell, helpful discussions about OpenID
  124. 124. http://www.flickr.com/people/marcelgermain/ The End
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