rare (and emergent) disciplines in the light of science studies
TRANSCRIPT
Rare (and emergent) disciplines in the light of science studies
Insights from TD1210 KnoweScape
Exploratory Workshop “Integrating the stake of rare disciplines at the European level” COST, Brussels, September 9, 2015
Andrea ScharnhorstDANS / eHumanities group
Royal Netherlands Academy of Arts and Sciences
Andrea Scharnhorst – “science located”
Information professionals• Collections, Information retrieval• WG 1 Phenomenology of
knowledge spaces• WG 4 Data curation & navigation
Social scientists• Simulating user behavior• WG 2 Theory of
knowledge spaces• WG 4 Data curation &
navigationComputer scientists • Semantic web, data models• WG 1 Phenomenology of Knowledge Spaces• WG 4 Data curation &navigation
Physicists, mathematicians
Digital humanities scholars• Collections, interactive design• WG 3 Visual analytics –
knowledge maps• WG 4 Data curation & navigation
Participating communities
• Structure & evolution of complex knowledge spaces, big data mining
• WG 2 Theory of knowledge spaces
• WG 3 Visual analytics – knowledge mapswww.knowescape.org
TD1210: Better understanding the dynamics of science – decay of attention and the problem with the Nobel Prize
Santo Fortunato at al. “The time lag between reporting a scientific discovery worthy of a Nobel prize and the awarding of the medal has increased, with waits of more than 20 years becoming common. If this trend continues, some candidates might not live long enough to attend their Nobel ceremonies.”
Fortunato, S. (2014). Prizes: Growing time lag threatens Nobels. Nature, 508(7495), 186. doi:10.1038/508186a
TD1210: Better understanding of the flaws of current methods to measure the impact of science – rankings, individual careers, interdisciplinarity
ETH Zurich, Ingo Scholtes, Frank Schweitzer“authors importance in the collaboration network is indicative for the citation success of the papers in the network “
Sarigöl, E., Pfitzner, R., Scholtes, I., Garas, A., & Schweitzer, F. (2014). Predicting Scientific Success Based on Coauthorship Networks. EPJ Data Science, 3 doi:10.1140/epjds/s13688-014-0009-x
TD1210: Better understanding innovative practices in science communication, altmetrics and other data sources
Torun, Veslava Osinska, Scientists on Facebook
Osińska, V., & Komendziński, T. (2014). Scientists on Facebook. Visualization of social networks in science [in Polish: Naukowcy na facebook- u . Wizualizacja sieci społecznych w nauce]. In E. Glowacka (Ed.), Contemporary aspects of communication and information. Problems, research, hypothesis (pp. 269–282). Toruń: NCU Publishing 2014. Retrieved from http://repozytorium.umk.pl/bitstream/handle/item/1779/VOsinska_HomoCommunicativus.pdf?sequence=1
TD1210: Better understanding the dynamics of science – the rise and fall of scientific fieldsParis, David Chavalarias“.. introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries …sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.
Chavalarias, D., & Cointet, J.-P. (2013). Phylomemetic patterns in science evolution--the rise and fall of scientific fields. PloS One, 8(2), e54847. doi:10.1371/journal.pone.0054847
TD1210: Simulation models for science policyMainz, Petra Ahrweiler..building and validating models takes a lot of time. We are too slow to really give policy advice. Models also rather produce scenarios, they give you options not a ready-made solution. Still, they are great to order your thinking
Ahrweiler, P., N. Gilbert and A. Pyka (eds., 2015, forthcoming): Joining complexity science and social simulation for innovation policy. Agent-based modelling using the SKIN platform. Cambridge Scholars Publishing, UK
TD1210: Better interfaces to large collections – visual analytics and semantic browsingOCLC, Rob Koopman, Shenghui Wang, et al.“a workflow which allows the user to browse live entities associated with 65 million articles ….by clicking through, a user traverses a large space of articles along dimensions of authors, journals, Dewey classes and words simultaneously. “
Koopman, R., Wang, S., Scharnhorst, A., & Englebienne, G. (2015). Ariadne’s Thread. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA ’15 (pp. 1833–1838). Digital Libraries. doi:10.1145/2702613.2732781
Rare and emergent disciplines are two different thingsthey share being comparative small
To determine what is rare we need to have a reference base to compare to
To determine what is emergent we need to look at the dynamics
TD1210: Better understanding the dynamics of science – diversification and merging of fieldsMartin Rosvall“.. With increasingly available data, networks and clustering tools have become important methods used to comprehend instances of these large-scale structures. But blind to the difference between noise and trends in the data, these tools alone must fail when used to study change. Only if we can assign significance to the partition of single networks can we distinguish structural changes from fluctuations and assess how much confidence we should have in the changes.”
Rosvall, M., & Bergstrom, C. T. (2010). Mapping change in large networks. PLoS ONE, 5(1). doi:10.1371/journal.pone.0008694
Courtesy of Kevin BoyackFields with < 100 journals in 1993BUT: first exploration, data not cross-checked
Stays small Stays small
Grows
Grows
List of full professors in the Netherlands with an expertise tag (D category) which is seldom
!
Rare expertise types among the full professorsIn The Netherlands BUT: we tag the person expertise build a hierarchical system…..
Problem of definition
Local, rich, not interoperable
Global, sparse, partly representative, partly curated Problem of data
Maps – yes we can!
Observatory is Another cup of tea!
Towards an observatoryIt looks as we actually don’t really know what we would need to measure – Problem of definitions and of
an inherent ambiguity and flux inherent to science
If we want to measure, we need to realize that the data available are not good enough – Heterogeneous, noisy data
Personal communication, Marnix van Berchum, DANS
- Inquire about the needs to measure (this workshop, questionnaires, …)- How would a system such as NARCIS, or CORDIS, or … need to look like to be of value?
Design ‘dream observatories’- Make an inventory of the existing data sources- Go for principles of LOD and Standards (CERIF) – see partnership here- Build a demonstrator ? Use as much as possible persistent identifier
If we have the data right, we are able to build visual interfaces to them. Visuals can already help us to get the data right!
Pointers to literature• Cassidy R. Sugimoto, Scott Weingart (2015) "The kaleidoscope of disciplinarity", Journal of Documentation, Vol. 71 Iss: 4,
pp.775 – 794, http://dx.doi.org/10.1108/JD-06-2014-0082, preprint: http://ella.slis.indiana.edu/~sugimoto/preprints/KaleidoscopeOfDisciplinarity.pdf
• Börner, K., Klavans, R., Patek, M., Zoss, A. M., Biberstine, J. R., Light, R. P., Lariviere, V., & Boyack, K. W. (2012). Design and update of a classification system: The UCSD map of science. PLOS One, 7(7), e39464. Published version
• Laudel, Grit and Weyer, Elke (2014) Where have All the Scientists Gone? Building Research Profiles at Dutch Universities and its Consequences for Research. In: Richard Whitley & Jochen Gläser (Eds.), Organizational Transformation and Scientific Change: The Impact of Institutional Restructuring on Universities and Intellectual Innovation. Research in the Sociology of Organizations (42). Emerald, 111 - 140. ISBN 9781783506842
• Mund, Carolin (2014) Identification of Emerging Scientific Topics in Bibliometric Databases. Dissertation, Karlsruhe (link: http://digbib.ubka.uni-karlsruhe.de/volltexte/1000042107 )
• Koopman, R., Wang, S., & Scharnhorst, A. (2015). Contextualization of topics - browsing through terms, authors, journals and cluster allocations. Arxiv Digital Libraries (cs.DL); Information Retrieval (cs.IR) http://arxiv.org/abs/1504.04208v1 TD1210
• Laudel, G., & Gläser, J. (2014). Beyond breakthrough research: Epistemic properties of research and their consequences for research funding. Research Policy, 43(7), 1204–1216. doi:10.1016/j.respol.2014.02.006
• Börner, Katy (2010) Atlas of Science, MIT Press; ~ (2014) Atlas of Knowledge, MIT Press