Do ResearchGate Scores create ghost academic reputations?

[EN] The academic social network site ResearchGate (RG) has its own indicator, RG Score, for its members. The high profile nature of the site means that the RG Score may be used for recruitment, promotion and other tasks for which researchers are evaluated. In response, this study investigates whether it is reasonable to employ the RG Score as evidence of scholarly reputation. For this, three different author samples were investigated. An outlier sample includes 104 authors with high values. A Nobel sample comprises 73 Nobel winners from Medicine and Physiology, Chemistry, Physics and Economics (from 1975 to 2015). A longitudinal sample includes weekly data on 4 authors with different RG Scores. The results suggest that high RG Scores are built primarily from activity related to asking and answering questions in the site. In particular, it seems impossible to get a high RG Score solely through publications. Within RG it is possible to distinguish between (passive) academics that interact little in the site and active platform users, who can get high RG Scores through engaging with others inside the site (questions, answers, social networks with influential researchers). Thus, RG Scores should not be mistaken for academic reputation indicators.Alberto Martin-Martin enjoys a four-year doctoral fellowship (FPU2013/05863) granted by the Ministerio de Educacion, Cultura, y Deporte (Spain). Enrique Orduna-Malea holds a postdoctoral fellowship (PAID-10-14), from the Polytechnic University of Valencia (Spain).Orduña Malea, E.; Martín-Martín, A.; Thelwall, M.; Delgado-López-Cózar, E. (2017). Do ResearchGate Scores create ghost academic reputations?. Scientometrics. 112(1):443-460. https://doi.org/10.1007/s11192-017-2396-9S4434601121Bosman, J. & Kramer, B. (2016). Innovations in scholarly communication—data of the global 2015–2016 survey. Available at: http://zenodo.org/record/49583 #. Accessed December 11, 2016.González-Díaz, C., Iglesias-García, M., & Codina, L. (2015). Presencia de las universidades españolas en las redes sociales digitales científicas: Caso de los estudios de comunicación. El profesional de la información, 24(5), 1699–2407.Goodwin, S., Jeng, W., & He, D. (2014). Changing communication on ResearchGate through interface updates. Proceedings of the American Society for Information Science and Technology, 51(1), 1–4.Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431.Hoffmann, C. P., Lutz, C., & Meckel, M. (2015). A relational altmetric? Network centrality on ResearchGate as an indicator of scientific impact. Journal of the Association for Information Science and Technology, 67(4), 765–775.Jiménez-Contreras, E., de Moya Anegón, F., & Delgado López-Cózar, E. (2003). The evolution of research activity in Spain: The impact of the National Commission for the Evaluation of Research Activity (CNEAI). Research Policy, 32(1), 123–142.Jordan, K. (2014a). Academics’ awareness, perceptions and uses of social networking sites: Analysis of a social networking sites survey dataset (December 3, 2014). Available at: http://dx.doi.org/10.2139/ssrn.2507318 . Accessed December 11, 2016.Jordan, K. (2014b). Academics and their online networks: Exploring the role of academic social networking sites. First Monday, 19(11). Available at: http://dx.doi.org/10.5210/fm.v19i11.4937 . Accessed December 11, 2016.Jordan, K. (2015). Exploring the ResearchGate score as an academic metric: reflections and implications for practice. Quantifying and Analysing Scholarly Communication on the Web (ASCW’15), 30 June 2015, Oxford. Available at: http://ascw.know-center.tugraz.at/wp-content/uploads/2015/06/ASCW15_jordan_response_kraker-lex.pdf . Accessed December 11, 2016.Kadriu, A. (2013). Discovering value in academic social networks: A case study in ResearchGate. Proceedings of the ITI 2013—35th Int. Conf. on Information Technology Interfaces Information Technology Interfaces, pp. 57–62.Kraker, P. & Lex, E. (2015). A critical look at the ResearchGate score as a measure of scientific reputation. Proceedings of the Quantifying and Analysing Scholarly Communication on the Web workshop (ASCW’15), Web Science conference 2015. Available at: http://ascw.know-center.tugraz.at/wp-content/uploads/2016/02/ASCW15_kraker-lex-a-critical-look-at-the-researchgate-score_v1-1.pdf . Accessed December 11, 2016.Li, L., He, D., Jeng, W., Goodwin, S. & Zhang, C. (2015). Answer quality characteristics and prediction on an academic Q&A Site: A case study on ResearchGate. Proceedings of the 24th International Conference on World Wide Web Companion, pp. 1453–1458.Martín-Martín, A., Orduna-Malea, E., Ayllón, J. M. & Delgado López-Cózar, E. (2016). The counting house: measuring those who count. Presence of Bibliometrics, Scientometrics, Informetrics, Webometrics and Altmetrics in the Google Scholar Citations, ResearcherID, ResearchGate, Mendeley & Twitter. Available at: https://arxiv.org/abs/1602.02412 . Accessed December 11, 2016.Martín-Martín, A., Orduna-Malea, E. & Delgado López-Cózar, E. (2016). The role of ego in academic profile services: Comparing Google Scholar, ResearchGate, Mendeley, and ResearcherID. Researchgate, Mendeley, and Researcherid. The LSE Impact of Social Sciences blog. Available at: http://blogs.lse.ac.uk/impactofsocialsciences/2016/03/04/academic-profile-services-many-mirrors-and-faces-for-a-single-ego . Accessed December 11, 2016.Matthews, D. (2016). Do academic social networks share academics’ interests?. Times Higher Education. Available at: https://www.timeshighereducation.com/features/do-academic-social-networks-share-academics-interests . Accessed December 11, 2016.Memon, A. R. (2016). ResearchGate is no longer reliable: leniency towards ghost journals may decrease its impact on the scientific community. Journal of the Pakistan Medical Association, 66(12), 1643–1647.Mikki, S., Zygmuntowska, M., Gjesdal, Ø. L. & Al Ruwehy, H. A. (2015). Digital presence of norwegian scholars on academic network sites-where and who are they?. Plos One 10(11). Available at: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142709 . Accessed December 11, 2016.Nicholas, D., Clark, D., & Herman, E. (2016). ResearchGate: Reputation uncovered. Learned Publishing, 29(3), 173–182.Orduna-Malea, E., Martín-Martín, A., & Delgado López-Cózar, E. (2016). The next bibliometrics: ALMetrics (Author Level Metrics) and the multiple faces of author impact. El profesional de la información, 25(3), 485–496.Ortega, Jose L. (2015). Relationship between altmetric and bibliometric indicators across academic social sites: The case of CSIC’s members. Journal of informetrics, 9(1), 39–49.Ortega, Jose L. (2016). Social network sites for scientists. Cambridge: Chandos.Ovadia, S. (2014). ResearchGate and Academia. edu: Academic social networks. Behavioral & Social Sciences Librarian, 33(3), 165–169.Thelwall, M., & Kousha, K. (2015). ResearchGate: Disseminating, communicating, and measuring Scholarship? Journal of the Association for Information Science and Technology, 66(5), 876–889.Thelwall, M. & Kousha, K. (2017). ResearchGate articles: Age, discipline, audience size and impact. Journal of the Association for Information Science and Technology, 68(2), 468–479.Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature, 512(7513), 126–129.Wilsdon, J., Allen, L., Belfiore, E., Campbell, P., Curry, S., Hill, S. et al. (2015). The Metric Tide: Independent Review of the Role of Metrics in Research Assessment and Management. HEFCE. Available at: http://doi.org/10.13140/RG.2.1.4929.1363 . Accessed December 11, 2016

Effective Rheology of Bubbles Moving in a Capillary Tube

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train from considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.Comment: 4 pages, 1 figur

Trait determinants of impulsive behavior: a comprehensive analysis of 188 rats

Impulsivity is a naturally occurring behavior that, when accentuated, can be found in a variety of neuropsychiatric disorders. The expression of trait impulsivity has been shown to change with a variety of factors, such as age and sex, but the existing literature does not reflect widespread consensus regarding the influence of modulating effects. We designed the present study to investigate, in a cohort of significant size (188 rats), the impact of four specific parameters, namely sex, age, strain and phase of estrous cycle, using the variable delay-to-signal (VDS) task. This cohort included (i) control animals from previous experiments; (ii) animals specifically raised for this study; and (iii) animals previously used for breeding purposes. Aging was associated with a general decrease in action impulsivity and an increase in delay tolerance. Females generally performed more impulsive actions than males but no differences were observed regarding delay intolerance. In terms of estrous cycle, no differences in impulsive behavior were observed and regarding strain, Wistar Han animals were, in general, more impulsive than Sprague-Dawley. In addition to further confirming, in a substantial study cohort, the decrease in impulsivity with age, we have demonstrated that both the strain and sex influences modulate different aspects of impulsive behavior manifestations.FEDER funds, through the Competitiveness Factors Operational Programme (COMPETE) and the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement as well as national funds, through the Foundation for Science and Technology (FCT) [projects POCI-01–0145-FEDER-007038, NORTE-01-0145-FEDER-000013, NORTE-01-0145-FEDER-000023 and PTDC/NEU-SCC/5301/2014]. Researchers were supported by FCT [grant numbers SFRH/BD/52291/2013 to ME and PD/BD/114117/2015 to MRG via Inter-University Doctoral Programme in Ageing and Chronic Disease, PhDOC; PDE/BDE/113601/2015 to PSM via PhD Program in Health Sciences (Applied) and Phd-iHES; SFRH/BD/109111/2015 to AMC; SFRH/BD/51061/2010 to MMC; SFRH/SINTD/60126/2009 to AM; SFRH/BD/98675/2013 to BC; IF/00883/2013 to AJR; IF/00111/2013 to AJS; SFRH/BPD/80118/2011 to HLA]info:eu-repo/semantics/publishedVersio

Deep sequencing analysis of the heterogeneity of seed and commercial lots of the bacillus Calmette-Guerin (BCG) tuberculosis vaccine substrain Tokyo-172

BCG, only vaccine available to prevent tuberculosis, was established in the early 20th century by prolonged passaging of a virulent clinical strain of Mycobacterium bovis. BCG Tokyo-172, originally distributed within Japan in 1924, is one of the currently used reference substrains for the vaccine. Recently, this substrain was reported to contain two spontaneously arising, heterogeneous subpopulations (Types I and II). The proportions of the subpopulations changed over time in both distributed seed lots and commercial lots. To maintain the homogeneity of live vaccines, such variations and subpopulational mutations in lots should be restrained and monitored. We incorporated deep sequencing techniques to validate such heterogeneity in lots of the BCG Tokyo-172 substrain without cloning. By bioinformatics analysis, we not only detected the two subpopulations but also detected two intrinsic variations within these populations. The intrinsic variants could be isolated from respective lots as colonies cultured on plate media, suggesting analyses incorporating deep sequencing techniques are powerful, valid tools to detect mutations in live bacterial vaccine lots. Our data showed that spontaneous mutations in BCG vaccines could be easily monitored by deep sequencing without direct isolation of variants, revealing the complex heterogeneity of BCG Tokyo-172 and its daughter lots currently in use

Factors affecting yield and gelling properties of agar

Agar, a gelatinous polysaccharide in the cell wall of many red algal species, is widely used as a gelling, thickening and stabilizing agent. The commercial value of seaweed is judged by their agar content and gel quality. Seaweed materials with higher agar yield and better gelling properties are desired due to the growing demand for agar in the global market. Agar biosynthesis in seaweeds is affected by genetic variations, developmental stages and environmental conditions, while different agar extraction techniques can also affect the yield and quality of agar. In this paper, the effects of different physiological states of seaweed, abiotic and biotic factors, seaweed storage and agar extraction techniques on the agar yield and gelling characteristics, are reviewed. This information is important as a guide for marine aquaculture of potential agarophytes and the possible effects of climate change on the stock of this natural resource

Measurement of prompt J/psi and beauty hadron production cross sections at mid-rapidity in pp collisions at root s=7 TeV

The ALICE experiment at the LHC has studied J/psi production at mid-rapidity in pp collisions at root s = 7 TeV through its electron pair decay on a data sample corresponding to an integrated luminosity L-int = 5.6 nb(-1). The fraction of J/psi from the decay of long-lived beauty hadrons was determined for J/psi candidates with transverse momentum p(t) > 1,3 GeV/c and rapidity vertical bar y vertical bar 1.3 GeV/c, vertical bar y vertical bar 1.3 GeV/c and vertical bar y vertical bar 1.3 GeV/c, vertical bar y vertical bar < 0.9) = 1.46 +/- 0.38 (stat.)(-0.32)(+0.26) (syst.) mu b. The results are compared to QCD model predictions. The shape of the p(t) and y distributions of b-quarks predicted by perturbative QCD model calculations are used to extrapolate the measured cross section to derive the b (b) over bar pair total cross section and d sigma/dy at mid-rapidity