This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
American Political Science Association (APSA). (2012). A guide to professional ethics in political science (2nd ed.). Washington, DC: The American Political Science Association. Retrieved on August 15, 2016, from www.apsanet.org/Portals/54/APSA%20Files/publications/ethicsguideweb.pdf.American Political Science Association (APSA)2012A guide to professional ethics in political science2ndWashington, DCThe American Political Science AssociationRetrieved on August 15, 2016, fromwww.apsanet.org/Portals/54/APSA%20Files/publications/ethicsguideweb.pdfSearch in Google Scholar
Ali, O.A., Emerich, D., Dranoff, G., & Mooney, D.J. (2009). In situ regulation of DC subsets and T cell mediates tumor regression in mice. Science Translational Medicine, 1(8), 8ra19.AliO.A.EmerichD.DranoffG.MooneyD.J.2009In situ regulation of DC subsets and T cell mediates tumor regression in miceScience Translational Medicine188ra1910.1126/scitranslmed.3000359287279120368186Search in Google Scholar
Bekhuis, T. (2006). Conceptual biology, hypothesis discovery, and text mining: Swanson’s legacy. Biomedical Digital Library, 3, 2.BekhuisT.2006Conceptual biology, hypothesis discovery, and text mining: Swanson’s legacyBiomedical Digital Library3210.1186/1742-5581-3-2145918716584552Search in Google Scholar
Blagosklonny, M.V., & Pardee, A.B. (2002). Conceptual biology: Unearthing the gems. Nature, 416(6879), 373.BlagosklonnyM.V.PardeeA.B.2002Conceptual biology: Unearthing the gemsNature416687937310.1038/416373a11919607Search in Google Scholar
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: W.W. Norton & Company Inc.BrynjolfssonE.McAfeeA.2014The second machine age: Work, progress, and prosperity in a time of brilliant technologiesNew YorkW.W. Norton & Company IncSearch in Google Scholar
Chansanchai, A. (2014). Microsoft research shows off advances in artificial intelligence with Project Adam. Microsoft Blog, July 14. Retrieved on September 2, 2016, from blogs.microsoft.com/next/2014/07/14/microsoft-research-shows-advances-artificial-intelligence-project-adam.ChansanchaiA.2014Microsoft research shows off advances in artificial intelligence with Project AdamMicrosoft Blog, July 14. Retrieved on September 22016fromblogs.microsoft.com/next/2014/07/14/microsoft-research-shows-advances-artificial-intelligence-project-adamSearch in Google Scholar
Chen, B., Ding, Y., & Wild, D. (2012). Assessing drug target association using semantic linked data. PLoS Computational Biology, 8(7), e1002574.ChenB.DingY.WildD.2012Assessing drug target association using semantic linked dataPLoS Computational Biology87e100257410.1371/journal.pcbi.1002574339039022859915Search in Google Scholar
Editorial (2009). Data’s shameful neglect. Nature, 461, 145.Editorial2009Data’s shameful neglectNature461145Search in Google Scholar
Ding, Y., Song, M., Han, J., Yu, Q., Yan, E., Lin, L., & Chambers, T. (2013). Entitymetrics: Measuring the impact of entities. PLoS One, 8(8), 1–14.DingY.SongM.HanJ.YuQ.YanE.LinL.ChambersT.2013Entitymetrics: Measuring the impact of entitiesPLoS One8811410.1371/journal.pone.0071416375696124009660Search in Google Scholar
Evans, J.A., & Foster, J.G. (2011). Metaknowledge. Science, 332(6018), 721–725.EvansJ.A.FosterJ.G.2011MetaknowledgeScience332601872172510.1126/science.120176521311014Search in Google Scholar
Flanagan, M. (2004). Barriers to the implementation of best practice in wound care. Wounds UK, 74–84. Retrieved on September 2, 2016, from www.woundsinternational.com/pdf/content_87.pdf.FlanaganM.2004Barriers to the implementation of best practice in wound careWounds UK7484Retrieved on September 2, 2016, fromwww.woundsinternational.com/pdf/content_87.pdfSearch in Google Scholar
Groth, P., & Moreau, L. (2013). PROV-Overview: An overview of the PROV family of documents. Retrieved on September 2, 2016, from www.w3.org/TR/prov-overview.GrothP.MoreauL.2013PROV-Overview: An overview of the PROV family of documentsRetrieved on September 2, 2016, fromwww.w3.org/TR/prov-overviewSearch in Google Scholar
Jinha, A.E. (2010). Article 50 million: An estimate of the number of scholarly articles in existence. Learned Publishing, 23(3), 258–263.JinhaA.E.2010Article 50 millionAn estimate of the number of scholarly articles in existenceLearned Publishing23325826310.1087/20100308Search in Google Scholar
Keiser, M.J., Setola, V., Irwin, J.J., Laggner, C., Abbas, A.I., Hufeisen, S.J., … Roth, B.L. (2009). Predicting new molecular targets for known drugs. Nature, 462(7270), 175–181.KeiserM.J.SetolaV.IrwinJ.J.LaggnerC.AbbasA.I.HufeisenS.J.RothB.L.2009Predicting new molecular targets for known drugsNature462727017518110.1038/nature08506278414619881490Search in Google Scholar
Kell, D.B. (2006). Metabolomics, modelling and machine learning in systems biology: Towards an understanding of the languages of cells. FEBS Journal, 273(5), 873–894.KellD.B.2006Metabolomics, modelling and machine learning in systems biology: Towards an understanding of the languages of cellsFEBS Journal273587389410.1111/j.1742-4658.2006.05136.x16478464Search in Google Scholar
Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press.KlahrD.2000Exploring science: The cognition and development of discovery processesCambridge, MAMIT PressSearch in Google Scholar
Kostoff, R.N. (2012). Literature-related discovery and innovation update. Technological Forecasting & Social Change, 79(4), 789–800.KostoffR.N.2012Literature-related discovery and innovation updateTechnological Forecasting & Social Change79478980010.1016/j.techfore.2012.02.002713182732287411Search in Google Scholar
Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., … Golub, T.R. (2006). The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929–1935.LambJ.CrawfordE.D.PeckD.ModellJ.W.BlatI.C.WrobelM.J.GolubT.R.2006The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and diseaseScience31357951929193510.1126/science.113293917008526Search in Google Scholar
McKinsey (2009). Hal Varian on how the web challenges managers. Retrieved on September 2, 2016, from www.mckinsey.com/insights/innovation/hal_varian_on_how_the_web_challenges_managers.McKinsey2009Hal Varian on how the web challenges managersRetrieved on September 2, 2016, fromwww.mckinsey.com/insights/innovation/hal_varian_on_how_the_web_challenges_managersSearch in Google Scholar
Mons, B., Van Haagen, H., Chichester, C., Hoen, P.B.T., Den Dunnen, J.T., … Schultes, E. (2011). The value of data. Nature Genetics, 43(4), 281–283.MonsB.Van HaagenH.ChichesterC.HoenP.B.T.Den DunnenJ.T.SchultesE.2011The value of dataNature Genetics43428128310.1038/ng0411-28121445068Search in Google Scholar
Moravcsik, A. (2014). Transparency: The revolution in qualitative research. Political Science & Politics, 47(1), 48–53.MoravcsikA.2014Transparency: The revolution in qualitative researchPolitical Science & Politics471485310.1017/S1049096513001789Search in Google Scholar
Oprea, T.I., Tropsha, A., Faulon, J., & Rintoul, M.D. (2007). Systems chemical biology. Nature Chemical Biology, 3, 447–450.OpreaT.I.TropshaA.FaulonJ.RintoulM.D.2007Systems chemical biologyNature Chemical Biology344745010.1038/nchembio0807-447273450617637771Search in Google Scholar
Schulz, K. (2011). What is distance reading. New York Times, Jan 24. Retrieved on September 2, 2016, from www.nytimes.com/2011/06/26/books/review/the-mechanic-muse-what-is-distant-reading.html?pagewanted=all&_r=0.SchulzK.2011What is distance readingNew York TimesJan 24. Retrieved on September 2, 2016, fromwww.nytimes.com/2011/06/26/books/review/the-mechanic-muse-what-is-distant-reading.html?pagewanted=all&_r=0Search in Google Scholar
Song, M., Han, N., Kim, Y., Ding, Y., & Chambers, T. (2013). Discovering implicit entity relation with the gene-citation-gene network. PLoS One, 8(12), e84639.SongM.HanN.KimY.DingY.ChambersT.2013Discovering implicit entity relation with the gene-citation-gene networkPLoS One812e8463910.1371/journal.pone.0084639386615224358368Search in Google Scholar
Spangler, S., Wilkins, A.D., Bachman, B.J., Nagarajan, M., Dayaram, T., Haas, P., … Lichtarge, O. (2014). Automated hypothesis generation based on mining scientific literature. Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp 1878–1886). New York, USA.SpanglerS.WilkinsA.D.BachmanB.J.NagarajanM.DayaramT.HaasP.LichtargeO.2014Automated hypothesis generation based on mining scientific literatureProceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining18781886New York, USA10.1145/2623330.2623667Search in Google Scholar
Swanson, D.R. (1986). Fish oil, Raynaud’s syndrome and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1), 7–18.SwansonD.R.1986Fish oil, Raynaud’s syndrome and undiscovered public knowledgePerspectives in Biology and Medicine30171810.1353/pbm.1986.00873797213Search in Google Scholar
Swanson, D.R., Smalheiser, N.R., & Bookstein, A. (2001). Information discovery from complementary literatures: Categorizing viruses as potential weapons. Journal of the American Society for Information Science and Technology, 52(10), 797–812.SwansonD.R.SmalheiserN.R.BooksteinA.2001Information discovery from complementary literatures: Categorizing viruses as potential weaponsJournal of the American Society for Information Science and Technology521079781210.1002/asi.1135Search in Google Scholar
Thomsen, M. (2015). Microsoft’s deep learning project outperforms humans in image recognition. Forbes, February 19. Retrieved on September 2, 2016, from www.forbes.com/sites/michaelthomsen/2015/02/19/microsofts-deep-learning-project-outperforms-humans-in-image-recognition.ThomsenM.2015Microsoft’s deep learning project outperforms humans in image recognitionForbes, February 19. Retrieved on September 2, 2016, fromwww.forbes.com/sites/michaelthomsen/2015/02/19/microsofts-deep-learning-project-outperforms-humans-in-image-recognitionSearch in Google Scholar
Upbin, B. (2013). IBM’s Watson gets its first piece of business in healthcare. Forbes, February 8. Retrieved on September 2, 2016, from www.forbes.com/sites/bruceupbin/2013/02/08/ibms-watson-gets-its-first-piece-of-business-in-healthcare.UpbinB.2013IBM’s Watson gets its first piece of business in healthcareForbes, February 8. Retrieved on September 2, 2016, fromwww.forbes.com/sites/bruceupbin/2013/02/08/ibms-watson-gets-its-first-piece-of-business-in-healthcareSearch in Google Scholar
You, J. (2015). Beyond the Turing test. Science, 347(6218), 116.YouJ.2015Beyond the Turing testScience347621811610.1126/science.347.6218.11625574001Search in Google Scholar
