{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T04:31:05Z","timestamp":1726029065668},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030186449"},{"type":"electronic","value":"9783030186456"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-18645-6_2","type":"book-chapter","created":{"date-parts":[[2019,5,20]],"date-time":"2019-05-20T10:15:30Z","timestamp":1558347330000},"page":"19-37","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Crystal\/Clear Pipeline for Applied Image Processing"],"prefix":"10.1007","author":[{"given":"Christopher J.","family":"Watkins","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Rosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Carroll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Ratcliffe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marko","family":"Ristic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Russell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Fazio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janet","family":"Newman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,4,16]]},"reference":[{"key":"2_CR1","unstructured":"Cinder \u201ccrystallographic tinder\u201d. https:\/\/research.csiro.au\/crystal\/user-guide\/c3-cinder\/ . Accessed 02 Jan 2019"},{"key":"2_CR2","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems March 2016. http:\/\/arxiv.org\/abs\/1603.04467"},{"key":"2_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","volume-title":"Computer Vision \u2013 ECCV 2006","author":"H Bay","year":"2006","unstructured":"Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404\u2013417. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023_32"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Bayes, F.R.S.: An Essay towards Solving a Problem in the Doctrine of Chances. Philos. Trans. R. Soc. Lond. 53(0), 370\u2013418 (1763). https:\/\/doi.org\/10.1098\/rstl.1763.0053","DOI":"10.1098\/rstl.1763.0053"},{"issue":"1","key":"2_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning deep architectures for AI. Found. Trends\u00ae Mach. Learn. 2(1), 1\u2013127 (2009). https:\/\/doi.org\/10.1561\/2200000006 . www.nowpublishers.com\/article\/Details\/MAL-006","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"2_CR6","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Sch\u00f6lkopf, B., Platt, J.C., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 153\u2013160. MIT Press (2007). http:\/\/papers.nips.cc\/paper\/3048-greedy-layer-wise-training-of-deep-networks.pdf"},{"key":"2_CR7","unstructured":"Rupp, B.: Garland Science - Book: Biomolecular Crystallography + 1. Garland Science, 1st edn. (2009). http:\/\/www.garlandscience.com\/product\/isbn\/9780815340812"},{"key":"2_CR8","unstructured":"Bernhardsson, E., Freider, E., Rouhani, A.: Luigi (2012). https:\/\/github.com\/spotify\/luigi"},{"key":"2_CR9","unstructured":"Bradski, G.: The OpenCV library. Dr. Dobb\u2019s J. Soft. Tools (2000)"},{"key":"2_CR10","volume-title":"Introduction to Protein Structure","author":"CI Br\u00e4nd\u00e9n","year":"1999","unstructured":"Br\u00e4nd\u00e9n, C.I., Tooze, J.: Introduction to Protein Structure. Garland Pub, Spokane (1999)"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Bruno, A.E.: Besra (2015). https:\/\/doi.org\/10.5281\/zenodo.60970 , https:\/\/www.researchgate.net\/publication\/309319298_Besra","DOI":"10.5281\/zenodo.60970"},{"issue":"6","key":"2_CR12","doi-asserted-by":"publisher","first-page":"e0198883","DOI":"10.1371\/journal.pone.0198883","volume":"13","author":"Andrew E. Bruno","year":"2018","unstructured":"Bruno, A.E., et al.: Classification of crystallization outcomes using deep convolutional neural networks. PLoS One 13(6) (2018). https:\/\/doi.org\/10.1371\/journal.pone.0198883","journal-title":"PLOS ONE"},{"issue":"23","key":"2_CR13","doi-asserted-by":"crossref","first-page":"12219","DOI":"10.1016\/S0021-9258(19)86452-9","volume":"254","author":"CW Carter","year":"1979","unstructured":"Carter, C.W., Carter, C.W.: Protein crystallization using incomplete factorial experiments. J. Biol. Chem. 254(23), 12219\u201312223 (1979). www.jbc.org\/cgi\/content\/short\/254\/23\/12219","journal-title":"J. Biol. Chem."},{"key":"2_CR14","unstructured":"Charbonneau, P.: Machine recognition of crystal outcomes (2018)"},{"issue":"3","key":"2_CR15","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995). https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach. Learn."},{"key":"2_CR16","unstructured":"Csurka, G., Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1\u201322 (2004). http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.72.604"},{"issue":"4","key":"2_CR17","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1107\/S0907444994002660","volume":"50","author":"R. Cudney","year":"1994","unstructured":"Cudney, R., Patel, S., Weisgraber, K., Newhouse, Y., McPherson, A.: Screening and optimization strategies for macromolecular crystal growth. Acta Crystallogr. Sect. D Biol. Crystallogr. 50(4), 414\u2013423 (1994). https:\/\/doi.org\/10.1107\/S0907444994002660 . http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/15299395","journal-title":"Acta Crystallographica Section D Biological Crystallography"},{"key":"2_CR18","unstructured":"Damien, A., et al.: TFLearn (2016)"},{"key":"2_CR19","unstructured":"Forcier, J.: Paramiko (2017)"},{"key":"2_CR20","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). http:\/\/arxiv.org\/abs\/1512.03385"},{"issue":"5786","key":"2_CR21","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504\u2013507 (2006). https:\/\/doi.org\/10.1126\/science.1127647 . www.science.sciencemag.org\/content\/313\/5786\/504","journal-title":"Science"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146\u20132153, September 2009. https:\/\/doi.org\/10.1109\/ICCV.2009.5459469","DOI":"10.1109\/ICCV.2009.5459469"},{"issue":"5802","key":"2_CR23","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1038\/290091a0","volume":"290","author":"B Julesz","year":"1981","unstructured":"Julesz, B.: Textons, the elements of texture perception and their interactions. Nature 290(5802), 91\u201397 (1981). https:\/\/doi.org\/10.1038\/290091a0 . www.nature.com\/doifinder\/10.1038\/290091a0","journal-title":"Nature"},{"key":"2_CR24","unstructured":"Kavukcuoglu, K., Sermanet, P., lan Boureau, Y., Gregor, K., Mathieu, M., Cun, Y.L.: Learning convolutional feature hierarchies for visual recognition. In: Lafferty, J.D., Williams, C.K.I., Shawe-Taylor, J., Zemel, R.S., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 23, pp. 1090\u20131098. Curran Associates, Inc. (2010). http:\/\/papers.nips.cc\/paper\/4133-learning-convolutional-feature-hierarchies-for-visual-recognition.pdf"},{"issue":"1","key":"2_CR25","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/BF00337288","volume":"43","author":"T Kohonen","year":"1982","unstructured":"Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59\u201369 (1982). https:\/\/doi.org\/10.1007\/BF00337288","journal-title":"Biol. Cybern."},{"key":"2_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-56927-2_6","volume-title":"Learning Vector Quantization","author":"T Kohonen","year":"2001","unstructured":"Kohonen, T.: Learning Vector Quantization. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/978-3-642-56927-2_6"},{"key":"2_CR27","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"issue":"1","key":"2_CR28","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s13321-016-0179-6","volume":"8","author":"S Lampa","year":"2016","unstructured":"Lampa, S., Alvarsson, J., Spjuth, O.: Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. J. Cheminformatics 8(1), 67 (2016). https:\/\/doi.org\/10.1186\/s13321-016-0179-6","journal-title":"J. Cheminformatics"},{"issue":"11","key":"2_CR29","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"issue":"7","key":"2_CR30","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1107\/S2053230X1401262X","volume":"70","author":"JR Luft","year":"2014","unstructured":"Luft, J.R., Newman, J., Snell, E.H.: Crystallization screening the influence of history on current practice. Acta Crystallogr. Sect. F Struct. Biol. Commun 70(7), 835\u201353 (2014). https:\/\/doi.org\/10.1107\/S2053230X1401262X . www.ncbi.nlm.nih.gov\/pubmed\/25005076","journal-title":"Acta Crystallogr. Sect. F Struct. Biol. Commun"},{"key":"2_CR31","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, vol. 3, pp. 807\u2013814, Haifa, Israel (2010). https:\/\/doi.org\/10.1.1.165.6419. http:\/\/www.cs.toronto.edu\/fritz\/absps\/reluICML.pdf"},{"issue":"3","key":"2_CR32","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1107\/S1744309112002618","volume":"68","author":"J Newman","year":"2012","unstructured":"Newman, J., et al.: On the need for an international effort to capture, share and use crystallization screening data. Acta Crystallogr. Sect. F Struct. Biol. Crystallization Commun. 68(3), 253\u2013258 (2012). https:\/\/doi.org\/10.1107\/S1744309112002618 . www.ncbi.nlm.nih.gov\/pubmed\/22442216","journal-title":"Acta Crystallogr. Sect. F Struct. Biol. Crystallization Commun."},{"issue":"12","key":"2_CR33","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1071\/CH14199","volume":"67","author":"J Newman","year":"2014","unstructured":"Newman, J., Peat, T.S., Savage, G.P.: What\u2019s in a name? Moving towards a limited vocabulary for macromolecular crystallisation. Aust. J. Chem. 67(12), 1813 (2014). https:\/\/doi.org\/10.1071\/CH14199 . www.publish.csiro.au\/?paper=CH14199","journal-title":"Aust. J. Chem."},{"issue":"10","key":"2_CR34","doi-asserted-by":"publisher","first-page":"2702","DOI":"10.1107\/S1399004714017581","volume":"70","author":"JT Ng","year":"2014","unstructured":"Ng, J.T., Dekker, C., Kroemer, M., Osborne, M., von Delft, F.: Using textons to rank crystallization droplets by the likely presence of crystals. Acta crystallogr. Sect. D, Biol. crystallogr. 70(10), 2702\u20132718 (2014). https:\/\/doi.org\/10.1107\/S1399004714017581 . www.ncbi.nlm.nih.gov\/pubmed\/25286854","journal-title":"Acta crystallogr. Sect. D, Biol. crystallogr."},{"issue":"2","key":"2_CR35","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1107\/S2059798315024687","volume":"72","author":"JT Ng","year":"2016","unstructured":"Ng, J.T., Dekker, C., Reardon, P., von Delft, F.: Lessons from ten years of crystallization experiments at the SGC. Acta Crystallogr. Sect. D Struct. Biol. 72(2), 224\u201335 (2016). https:\/\/doi.org\/10.1107\/S2059798315024687 . www.ncbi.nlm.nih.gov\/pubmed\/26894670","journal-title":"Acta Crystallogr. Sect. D Struct. Biol."},{"key":"2_CR36","unstructured":"Ratcliffe, D., Carroll, T., Watkins, C., Newman, J.: CSIRO data access portal - crystallisation images from C3 (2016). https:\/\/data.csiro.au\/dap\/landingpage?pid=csiro:20158&v=3&d=true"},{"key":"2_CR37","unstructured":"Roberts, M., Torres, G.: PySlurm (2017). https:\/\/pyslurm.github.io\/"},{"key":"2_CR38","unstructured":"Rolnick, D., Veit, A., Belongie, S., Shavit, N.: Deep learning is robust to massive label noise, May 2017. http:\/\/arxiv.org\/abs\/1705.10694"},{"key":"2_CR39","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, September 2014. http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"2_CR40","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1214\/12-AOS1000","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014). https:\/\/doi.org\/10.1214\/12-AOS1000","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR41","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826, June 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"2_CR42","unstructured":"Szegedy, C., et al.: Going deeper with convolutions, September 2014. http:\/\/arxiv.org\/abs\/1409.4842"},{"issue":"8","key":"2_CR43","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832\u2013844 (1998). https:\/\/doi.org\/10.1109\/34.709601","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR44","first-page":"2579","volume":"9","author":"L Maaten Van Der","year":"2008","unstructured":"Van Der Maaten, L., Hinton, G.: Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008). www.jmlr.org\/papers\/volume9\/vandermaaten08a\/vandermaaten08a.pdf","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR45","unstructured":"Vanhoucke, V.: Automating the evaluation of crystallization experiments. https:\/\/github.com\/tensorflow\/models\/tree\/master\/research\/marco (2018)"},{"key":"2_CR46","unstructured":"Watkins, C.J.: C3 Computer vision algorithms (2017). https:\/\/data.csiro.au\/dap\/landingpage?pid=csiro:29414"},{"key":"2_CR47","unstructured":"Watkins, C.J.: C4\u2013C3 Classification pipeline (2018). https:\/\/data.csiro.au\/dap\/landingpage?pid=csiro:29413"},{"issue":"1","key":"2_CR48","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1107\/S0021889807049308","volume":"41","author":"D Watts","year":"2008","unstructured":"Watts, D., Cowtan, K., Wilson, J.: IUCr: automated classification of crystallization experiments using wavelets and statistical texture characterization techniques. J. Appl. Crystallogr. 41(1), 8\u201317 (2008). https:\/\/doi.org\/10.1107\/S0021889807049308","journal-title":"J. Appl. Crystallogr."},{"key":"2_CR49","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks, November 2016. http:\/\/arxiv.org\/abs\/1611.05431","DOI":"10.1109\/CVPR.2017.634"}],"container-title":["Lecture Notes in Computer Science","Supercomputing Frontiers"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-18645-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,18]],"date-time":"2022-09-18T05:04:51Z","timestamp":1663477491000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-18645-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030186449","9783030186456"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-18645-6_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SCFA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Supercomputing Frontiers","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 March 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 March 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scfa2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.sc-asia.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"33","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"6","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}