Rare (and emergent) disciplines in the light of science studies
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Andrea Scharnhorst. Insights from TD1210. presentation given at Exploratory Workshop “Integrating the stake of rare disciplines at the European level” COST, Brussels, September 9, 2015
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Rare (and emergent) disciplines in the light of science studies
- 1. 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 Scharnhorst DANS / eHumanities group Royal Netherlands Academy of Arts and Sciences
- 2. Andrea Scharnhorst – “science located”
- 3. MESUR Project Clickstream map of science www.mesur.org
- 4. Informa on Professionals/ Informa on Scien sts Social Scien sts Computer Scien sts Physics/Mathema cs Digital Humani es 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 & navigation Computer 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 maps www.knowescape.org
- 5. 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
- 6. 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
- 7. 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
- 8. TD1210: Better understanding the dynamics of science – the rise and fall of scientific fields Paris, 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
- 9. TD1210: Simulation models for science policy Mainz, 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
- 10. TD1210: Better interfaces to large collections – visual analytics and semantic browsing OCLC, 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
- 11. Rare and emergent disciplines are two different things they 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
- 12. TD1210: Better understanding the dynamics of science – diversification and merging of fields Martin 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
- 13. Courtesy of Kevin Boyack Fields with < 100 journals in 1993 BUT: first exploration, data not cross-checked Stays small Stays small Grows Grows
- 14. List of full professors in the Netherlands with an expertise tag (D category) which is seldom ! Rare expertise types among the full professors In The Netherlands BUT: we tag the person expertise build a hierarchical system …..
- 16. Local, rich, not interoperable Global, sparse, partly representative, partly curated Problem of data
- 17. Maps – yes we can! Observatory is Another cup of tea!
- 18. Towards an observatory It 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!
- 19. 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
Editor's Notes
- To shortly introduce myself, I use a so-called science map (Boyack, Klavans). On this map we see in different colors 13 disciplines. The map has been produced from networks between articles, and journals, which are are related by citations, or/and by similar word use. That the map is made from a ‘journal perspective’ explains why the humanities are Tasmania. I like this map, because it shows that in academia everything is connected to each other. There are no two separated continents of hard and soft sciences. I sometimes make the joke that I did a long journey from physics to philosophy of science, but actually – because this is Mercator projection of a network rendered on a globe, philosophy and physics are just around a corner. The ‘land’ bridge is mathematics. For the topic of our workshop it also shows: one cannot just cut out some pieces without effecting the whole ecosystem of academia. Our Action produces this kind of maps.
- It is not in the visualisation (only), it is in the data Other data show the importance of humanities, download pdf ~ reading
- This slide depicts our COST Action in a nutshell. As you can see we are a rather diverse crowd, encompassing librarians and physicists. Why so? Information spaces are large, they are dynamic – they are often represented at networks -> physics Visualization and analysis, but also use of maps and models for science policy -> social scientists, agent-based models, simulations To map information we often always in need of data, we also look for new data models to make data interoperable and thus scalable -> Semantic web Science policy is one stake holder; libraries and archives are others -> digital collections, and researchers working with them; librarian and archivists I also brought some more artifacts -> on the wall
- I picked some research from our Action relevant for this workshop; they all around science dynamics.
- It is not only about sophisticated data analysis; the way we measure science, define fields influences funding, personal careers. Only step by step a discourse starts between content providers as Thomson Reuters, indicator makers, science policy makers, and data scientists – ETH Zurich.
- Our members are active in many countries; upcoming workshop in Warsaw Sep 17
- There are different methods to depict the evolution of science; this is one A recent workshop (actually a series) in Berlin first time made people comparing their algorithms on the same dataset
- Knowescape is not only on understanding, it is also about action/intervention/support Lessons learned Complex – expectation management, but also not good to give up
- It is not only about science policy, it is also about actual helping to navigate the ocean of information New interfaces
- Let me continue with some thoughts based on the work in our Action. To determine what is rare we need to have a comparison, a reference scheme. The German definition of “small fields” refers to the total number of universities in Germany, for instance. What is small for Germany, might be rather big for the Netherlands. To determine what is emergent we need to look at the dynamics.
- Kevin Boyack: “Those new disciplines that do form have a good chance to become emergent (bioinformatics and nanoscience are recent examples), but those smaller disciplines that have been around a long time are likely to stay small. My perception is that most disciplines are adequately described by inertia. Small disciplines stay small, larger ones have steady but modest growth, and then there are the few emergent ones. The more granular the classification, the more variance there is from the standard inertia patterns. Emergent topics seem to be much more common than emergent disciplines.”
- So, we can find traces, but there is a difference between finding traces and building an observatory
- Expertise, numbers of journals related to an area; what is actually a field – the problem of definition But let me use slides of one of our invited experts – who wrote an essay about the discourse of disciplines. I like those lists of the existing ‘word soup’ as Scott Weingart called it.
- If we want to use metric approaches for an envisioned observatory for small fields, we need to start with an inventory of structured data available. Here we find the following situation: we have rich information locally, often not interoperable, often not systematically recorded over time. We have this information about institutions, funding, staff, and output. We have rather sparse information on a global level. On all levels we are confronted with the fact that information about Higher Education is collected on different aggregation levels by different agencies: statistical offices, ministries, couple or roof organizations. Data are also collected from different perspectives. If we take the most prevalent model about the science, being a self-organized, autonomous enterprise which practices are rather in a black box. Than we have information on input: money, staff, students, …. on the one side and information on output on the other side (publications, patents, citations), and there is almost no relation between them. Let me illustrate this for the Netherlands – as far as I’m aware of it. We have NARCIS as aggregator of all these types of information. From within, the information we have on stuff is still curated. We are not complete in projects. We do exchange information with NWO – but there database have other schemes. VSNU has its own database. We get better in publications, but we are also kind of bound because we harvest what content providers (the universities and others) offer to be harvested. The big player in publications and citations is CWTS at Leiden. In 2013 we published an article about disambiguation of Dutch researchers and mapped our detailed information of persons with their information on persons and publications. We have a collaboration pending to look into the whole publication performance profile of the Netherlands and its institutional counterpart – we postponed this due to lack of funding. These are only organisations already aggregating on the level of countries. Each university has its own RIS. We used to have METIS. The KNAW is now operating with PURE, a commercial product of Elsevier, and I sometimes half seriously use to say it is beautiful to get information in even of a rather lay person, but you never see it back …. The only university of which I know – Nick Veenstra was the responsible librarian which applied a Linked Open Data format – vivoweb – is the University of Eindhoven. We do find Dutch researchers back what concerns their projects in CORDIS, and even more promising in The EU Open Data Portal. But we don’t harvest from CORDIS yet, because we harvest from universities in NL. On the European level we have projects as OpenAire. Globally, initiatives as ORCID try to make the persons traceable through all information. I know if no registry for chairs, or courses on the European level or worldwide. The UNESCO Institute for Statistics might be an organisation to have some of this information.
- What concerns methods to analyze and visualize knowledge dynamics; the emergence of fields; the evolution of ideas; the research landscape we – as community – have methods and tools at hand. The crucial element are data. Maps as the one displayed are too often artifacts of a singular encounter between database information and visual analytics.
- For an envisioned observatory one would need to 0) agree of definitions Continue to make questionnaires – they give you immediately the expert perception of rare/emergent/endagered Use this information as pointers, threads to seek metric information to match them; and for this metric sub-cycle in an envisioned iterative search one would need to Make an inventory of existing data sources Seek for handles for interoperability, and design a data model based on Linked Data principles Build an alliance with players as EUROCRIS Use as much as possible persistent identifiers for the information.