課程資訊
課程名稱
開放科學研討
Seminar in Open Science 
開課學期
109-2 
授課對象
文學院  圖書資訊學研究所  
授課教師
鄭瑋 
課號
LIS8027 
課程識別碼
126 D1050 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期三A,B,C(18:25~21:05) 
上課地點
圖資會議室 
備註
初選不開放。先修課程:研究資料基礎架構。有意選修者請先email詢問授課教師。
總人數上限:5人 
 
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課程概述

This course introduces graduate students to the key concepts involving open science with particular focal points on reproducibility research from a library and information science scope. Mostly in English, this course comprises of lectures, weekly presentations, group discussions, and research writings for eventual academic conference plus journals publishing. 

課程目標
Upon taking this course, students are expected to be able to:
- Detail both the challenges and opportunities involving open science.
- From a research data infrastructure premise, articulate concepts involving the research data lifestyle and its intricacies.
- From a library and information science view, empower a comparative view of the respective open science-related information policies of Taiwan and worldwide.
- Engage in an academic conference paper developing, authoring and submitting process in major library and information science academic conferences – preferably the yearly Association for Information Science and Technology (ASIS&T) or iConference. 
課程要求
Prior to this course, students are suggested to have taken research data infrastructure-related courses or related open science seminars at the graduate level. Students are also expected to be reading to discuss his/her monthly research progress check-ups with the instructor. The final term project involves the aforesaid academic conference paper and/or poster that must be “conference publication-ready” by the end of the semester. 
Office Hours
 
參考書目
Open science
-Origin and History:
Breznau, N. (2021). Does sociology need open science?. Societies, 11(1), 9.
David, P. (2003). The economic logic of ‘open science’ and the balance between private property rights and the public domain in scientific data and information: A primer. In J. M. Esanu, & P. F. Uhlir (eds.), The role of scientific and technical data and information in the public domain: Proceedings of a symposium. Washington, D.C: National Academies Press.
Nosek, B. A., & Bar-Anan, Y. (2012). Scientific utopia: I. Opening scientific communication. Psychological Inquiry, 23(3), 217-243.
David, P. (2008). The historical origins of ‘open science’: An essay on patronage, reputation and common agency contracting in the scientific revolution. Capitalism and Society, 3(2), 5.
Fecher B., & Friesike S. (2014). Open science: One term, five schools of thought. In Bartling S., & Friesike S. (eds.), Opening science. Springer, Cham.
Molloy, J. C. (2011). The open knowledge foundation: Open data means better science. PLoS Biology, 9(12): e1001195.
Vicente-Saez, R., & Martinez-Fuentes, C. (2018). Open science now: A systematic literature review for an integrated definition. Journal of Business Research, 88, 428-436.
Hey, A., Tansley S., & Tolle, K. M. (2009). The fourth paradigm: Data-intensive scientific discovery. Redmond, Washington: Microsoft Research.


-Policies and Open Government Data:
Albornoz, D., Huang, M., Martin, I., Mateus, M., TourU+E9, A. & Chan, L. (2018). Framing power: Tracing key discourses in open science policies. ELPUB 2018, Toronto, Canada.
Bertot, J., Gorham, U., Jaeger, P., Sarin, L., & Choi, H. (2014). Big data, open government and e-government: Issues, policies and recommendations. Information Polity, 19(1, 2), 5-16.
Burgelman, J., Pascu, C., Szkuta, K., Von Schomberg, R., Karalopoulos, A., Repanas, K., & Schouppe, M. (2019). Open science, open data, and open scholarship: European policies to make science fit for the 21st century. Frontiers in Big Data, 2.
Chatfield, A., & Reddick, C. (2018). The role of policy entrepreneurs in open government data policy innovation diffusion: An analysis of Australian Federal and State Governments. Government Information Quarterly, 35(1), 123-134.
Huang, H., Liao, Z.-P., Liao, H.-C., & Chen, D.-Y. (2020). Resisting by workarounds: Unraveling the barriers of implementing open government data policy. Government Information Quarterly, 37(4), 101495.
Yang, T.-M., Lo, J, & Shiang, J. (2015). To open or not to open? Determinants of open government data. Journal of Information Science, 41(5), 596-612.

Replication and Reproducibility
-Overview:
Christakis, D. A., & Zimmerman, F. J. (2013). Rethinking reanalysis. JAMA: The Journal of the American Medical Association, 310(23), 2499-2500.
Descriptions of the replications typologies. (n.d.). Grupo de InvestigaciU+F3n en IngenierU+EDa del Software EmpU+EDrica. Retrieved from http://www.grise.upm.es/sites/extras/10/
Freedman, L. P., Cockburn, I. M., & Simcoe, T. S. (2015). The economics of reproducibility in preclinical research. PLoS Biol, 13(6), e1002165.
Munafo, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., ... & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour 1, 0021. doi: 10.1038/s41562-016-0021
Mellor, D. (2017). Promoting reproducibility with registered reports. Nature Human Behaviour, 1.

-Technological Infrastructures:
Benchoufi, M., Altman, D., & Ravaud, P. (2019). From clinical trials to highly trustable clinical trials: Blockchain in clinical trials, a game changer for improving transparency?. Frontiers in Blockchain, 2, 23.
Janowicz, K., Regalia, B., Hitzler, P., Mai, G., Delbecque, S., FrU+F6hlich, M., Martinent, P., & Lazarus, T. (2018). On the prospects of blockchain and distributed ledger technologies for open science and academic publishing. Semantic web, 9(5), 545–555.
Jeng, W., Wang, S. H., Chen, H. W., Huang, P. W., Chen, Y. J., & Hsiao, H. C. (2020). A decentralized framework for cultivating research lifecycle transparency. PLoS One, 15(11), e0241496.
Rouder, J. N. (2016). The what, why, and how of born-open data. Behavior research methods, 48(3), 1062-1069.
Stodden, V., Krafczyk, M. S., & Bhaskar, A. (2018). Enabling the verification of computational results: An empirical evaluation of computational reproducibility. Proceedings of the First International Workshop on Practical Reproducible Evaluation of Computer Systems, 1-5.

-Institutional services:
Vitale, C. R. (2016). Is research reproducibility the new data management for libraries?. Bulletin of the Association for Information Science and Technology, 42(3), 38-41.
Bartling, S. (2018). Blockchain for science and knowledge creation. Blockchain for Science.
Lyon, L. (2016). Transparency: The emerging third dimension of open science and open data. LIBER Quarterly, 25(4), 153-171. doi:10.18352/lq.10113
Lyon, L., Jeng, W., & Mattern, E. (2020). Developing the tasks-toward-transparency (T3) model for research transparency in open science using the lifecycle as a grounding framework. Library & Information Science Research, 42(1), 100999.
Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., ..., & Yarkoni, T. (2015). Promoting an open research culture: Author guidelines for journals could help to promote transparency, openness, and reproducibility. American Association for the Advancement of Science, 348(6242), 1422-1425.
Sayre, F., & Riegelman, A. (2019). Replicable services for reproducible research: A model for academic libraries. College & Research Libraries, 80(2), 260.
Stodden, V., Seiler, J., & Ma, Z. (2018). An empirical analysis of journal policy effectiveness for computational reproducibility. Proceedings of the National Academy of Sciences, 115(11), 2584-2589. pmid:29531050

Participatory Science & Citizen Science
邱文聰(2010)。科學研究自由與第三波科學民主化的挑戰。2009科技發展與法律規範雙年刊,61-115。
黃志堅(2017)。公民科學家計畫的過去與未來。臺灣林業,43(4), 51-60。
Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009). Citizen science: A developing tool for expanding science knowledge and scientific literacy. BioScience, 59(11), 977-984.
Callaghan, C. T., Rowley, J. L., Cornwell, W. K., Poore, A., & Major, R. E. (2019). Improving big citizen science data: Moving beyond haphazard sampling. PLoS Biology, 17(6), E3000357.
Cohen, C., Cheney, L., Duong, K., Lea, B., & Unno, Z. (2015). Identifying opportunities in citizen science for academic libraries. Issues in Science and Technology Librarianship, 79, 1-13.
Collins, H., & Evans, R. (2002). The third wave of science studies: Studies of expertise and experience. Social Studies of Science, 32(2), 235-296. Retrieved from http://www.jstor.org/stable/ 3183097
Cox, J., Oh, E., Simmons, B., Lintott, C., Masters, K., Greenhill, A., ... & Holmes, K. (2015). Defining and measuring success in online citizen science: A case study of Zooniverse projects. Computing in Science & Engineering, 17(4), 28-41.
Hetland, P., Pierroux, P., & Esborg, L. (2020). A history of participation in museums and archives: Traversing citizen science and citizen humanities. Milton: Taylor & Francis Group.
Haythornthwaite, C. (2009). Crowd and communities: Light and heavyweight models of peer production. Proceedings of the 42nd Hawaii International Conference on System Sciences.
Wilkinson, M. D. et al. (2016). Comment: The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3: 160018. doi: 10.1038/sdata.2016. 18
Kelling, S., Fink, D., Sorte, F., Johnston, A., Bruns, N., & Hochachka, W. (2015). Taking a ‘Big Data’ approach to data quality in a citizen science project. Ambio, 44(4), S601-S611.

Data Sharing and Reuse
Cox, A. M., Kennan, M. A., Lyon, E. J., Pinfield, S., & Sbaffi, L. (2019). Progress in research data services: An international survey of university libraries. International Journal of Digital Curation, 14(1), 126-135.
Fecher, B., Friesike, S., & Hebing, M. (2015). What drives academic data sharing? PLoS One, 10(2): e0118053. doi:10.1371/journal.pone.0118053
Zuiderwijk, A., Shinde, R., & Jeng, W. (2020). What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption. PLoS One, 15(9): e0239283.

Systematic Literature Review: Approaches and Samples
Dixon-Woods, M., Bonas, S., Booth, A., Jones, D. R., Miller, T., Sutton, A. J., ... & Young, B. (2006). How can systematic reviews incorporate qualitative research? A critical perspective. Qualitative research, 6(1), 27-44.
Fecher, B., Friesike, S., & Hebing, M. (2015). What drives academic data sharing? PLoS One, 10(2): e0118053. doi:10.1371/journal.pone.0118053
Zuiderwijk, A., Shinde, R., & Jeng, W. (2020). What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption. PLoS One, 15(9): e0239283. 
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