Erratum to: The sponge microbiome project
Moitinho-Silva, Lucas ; Nielsen, Shaun ; Amir, Amnon ; Gonzalez, Antonio ; Ackermann, Gail L. ; Cerrano, Carlo ; Astudillo-Garcia, Carmen ; Easson, Cole ; Sipkema, Detmer ; Liu, Fang ; Steinert, Georg ; Kotoulas, Giorgos ; McCormack, Grace P. ; Feng, Guofang ; Bell, James J. ; Vicente, Jan ; Björk, Johannes R. ; Montoya, Jose M. ; Olson, Julie B. ; Reveillaud, Julie ; Steindler, Laura ; Pineda, Mari Carmen ; Marra, Maria V. ; Ilan, Micha ; Taylor, Michael W. ; Polymenakou, Paraskevi ; Erwin, Patrick M. ; Schupp, Peter J. ; Simister, Rachel L. ; Knight, Rob ; Thacker, Robert W. ; Costa, Rodrigo ; Hill, Russell T. ; Lopez-Legentil, Susanna ; Dailianis, Thanos ; Ravasi, Timothy ; Hentschel, Ute ; Li, Zhiyong ; Webster, Nicole S. ; Thomas, Torsten - \ 2018
GigaScience 7 (2018)12. - ISSN 2047-217X
The sponge microbiome project
Moitinho-Silva, Lucas ; Nielsen, Shaun ; Amir, Amnon ; Gonzalez, Antonio ; Ackermann, Gail L. ; Cerrano, Carlo ; Astudillo-Garcia, Carmen ; Easson, Cole ; Sipkema, Detmer ; Liu, Fang ; Steinert, Georg ; Kotoulas, Giorgos ; McCormack, Grace P. ; Feng, Guofang ; Bell, James J. ; Vicente, Jan ; Björk, Johannes R. ; Montoya, Jose M. ; Olson, Julie B. ; Reveillaud, Julie ; Steindler, Laura ; Pineda, Mari Carmen ; Marra, Maria V. ; Ilan, Micha ; Taylor, Michael W. ; Polymenakou, Paraskevi ; Erwin, Patrick M. ; Schupp, Peter J. ; Simister, Rachel L. ; Knight, Rob ; Thacker, Robert W. ; Costa, Rodrigo ; Hill, Russell T. ; Lopez-Legentil, Susanna ; Dailianis, Thanos ; Ravasi, Timothy ; Hentschel, Ute ; Li, Zhiyong ; Webster, Nicole S. ; Thomas, Torsten - \ 2017
GigaScience 6 (2017)10. - ISSN 2047-217X
16S rRNA gene - Archaea - Bacteria - Marine sponges - Microbial diversity - Microbiome - Symbiosis
Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal, and environmental scales. Samples from marine sponges (n = 3569 specimens), seawater (n = 370), marine sediments (n = 65) and other environments (n = 29) were collected from different locations across the globe. This dataset incorporates at least 268 different sponge species, including several yet unidentified taxa. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1.1 billion sequences) were processed and clustered with (i) a standard protocol using QIIME closed-reference picking resulting in 39 543 operational taxonomic units (OTU) at 97% sequence identity, (ii) a de novo clustering using Mothur resulting in 518 246 OTUs, and (iii) a new high-resolution Deblur protocol resulting in 83 908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications, and metadata are provided. This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host specificity, convergent evolution, environmental drivers of microbiome structure, and the sponge-associated rare biosphere.
|Inter-organizational network analysis in synergy parks
Nuhoff-Isakhanyan, G. ; Wubben, E.F.M. ; Omta, S.W.F. - \ 2014
Organizational collaborations are important means for organizations to access new resources and enhance the sustainable performance. Recent examples of inter-organizational collaborations towards more sustainable production are synergy parks, such as eco-industrial parks and agroparks. Synergy parks are collaborations among organizations across different sectors, mainly from agriculture and industry, aiming at enhanced economic and environmental performance, sustainable agri-food and bio-energy production through exchanging waste and by-products, creating production synergies. Because synergy parks connect organizations in their non-core business activities, these organizations are not always keen in the realization of synergy parks. A synergy park consists of multiple organizations from various sectors linked through multiple ties, its coordination can be explained by means of organizational network theory (Van de Ven & Fery, 1980). Consequently, a synergy park can be seen as a network where companies are the nodes and their collaborations the ties. Companies with direct ties, can affect the behavior of one another (Rowley, 1997). Recently more and more scholars use network analysis in understanding firms, stakeholders, and their social and behavioral phenomena (Ahuja, 2000; Ahuja, et al., 2009; Corsaro, et al., 2012; Gulati, 2007; Gulati, et al., 2000). Theories that discuss organizational networks, however, pay more attention to relations at dyadic level. Network analysis use in understanding firms, stakeholders, and their social and behavioral phenomena beyond dyadic level is slowly increasing (Ackermann & Eden, 2011; Frooman, 1999; Rowley, 1997). It provides scholars new insights to develop the inter-organizational network theory, to further it from dyadic relationship and examine systems of dyadic interactions capturing the influence of multiple and interdependent relations on network development. The purpose of the study is to understand how the structure of inter-organizational networks impact the realization of synergy parks by analyzing network attributes. In this study we answer the following questions: What is the impact of the network structure attributes (size, type of relation, centrality, and density) on realization of inter-organizational collaborations, such as a synergy park? What alternative network structures are effective in different inter-organizational collaborations? We suggest the following propositions: 1) The relation between the size of the network and the potential of a synergy park realization has an inverse convex shape (n shape) 2) Companies connected with both formal and informal ties have stronger and enduring relationships than the ones connected with formal ties only. 3) Decentralized and dense network structures are more suited for the realization of a synergy park if the set of involved companies are more heterogeneous. We conducted cross-case analysis in three synergy parks through using mixed qualitative and quantitative methods. The unit of analysis is the exchange relationship among the organizations within the networks. We focus on formal, informal, and trust related relations. We identified the boundary spanners in each organization, and asked managers who are the most knowledgeable about the relation of other organizations in the parks. These persons are formally or informally responsible for managing the collaborative relationships with other organizations. The main method of data collection was semi-structured interviews. The network survey has complex design comparing to standard surveys, therefore, we decided to interview each respondent personally by using ONA online survey tools. Concerning to network ties, we gather value and binary data. Each tie among the same companies have been measured and analyzed separately, and compared with one another. The data is coded and analyzed by using UCINET network analysis software (Borgatti, et al., 2002; Hanneman & Riddle, 2005). Networks are framed and analyzed per synergy park separate, which is followed by the analysis across networks. The discussion and the conclusion will be presented in the full paper. Reference Ackermann, F., & Eden, C., 2011. Strategic Management of Stakeholders: Theory and Practice. Long Range Planning, 44(3): 179-196. Ahuja, G., 2000. Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study. Administrative Science Quarterly, 45(3): 425-455. Ahuja, G., Polidoro, F., & Mitchell, W., 2009. Structural homophily or social asymmetry? The formation of alliances by poorly embedded firms. Strategic Management Journal, 30(9): 941-958. Borgatti, S. P., Everett, M. G., & Freeman, L. C., 2002. UCINET for Windows, Version 6.59: Software for Social Network Analysis. Harvard, MA Analytic Technologies. Corsaro, D., Cantu, C., & Tunisini, A., 2012. Actors' Heterogeneity in Innovation Networks. Industrial Marketing Management, 41(5): 780-789. Frooman, J., 1999. Stakeholder influence strategies. Academy of Management Review, 24(2): 191-205. Gulati, R., 2007. Managing network resources: alliances, affiliations and other relational assets. Oxford: Oxford University Press. Gulati, R., Nohria, N., & Zaheer, A., 2000. Strategic networks. Strategic Management Journal, 21(3): 203-215. Hanneman, R. A., & Riddle, M., 2005. Introduction to Social Network Methods. Riverside CA: University of California. Rowley, T. J., 1997. Moving beyond dyadic ties: A network theory of stakeholder influences. Academy of Management Review, 22(4): 887-910. Van de Ven, A. H., & Fery, D. L., 1980. Measuring and Assessing Organizations: John Wiley & Sons, Inc.
Measuring Social Value Orientation
Murphy, R.O. ; Ackermann, K.A. ; Handgraaf, M.J.J. - \ 2011
Judgment and Decision Making 6 (2011)8. - ISSN 1930-2975 - p. 771 - 781.
individual-differences - resource dilemmas - risk - game - regression - behavior - motives - own
Narrow self-interest is often used as a simplifying assumption when studying people making decisions in social contexts. Nonetheless, people exhibit a wide range of different motivations when choosing unilaterally among interdependent outcomes. Measuring the magnitude of the concern people have for others, sometimes called Social Value Orientation (SVO), has been an interest of many social scientists for decades and several different measurement methods have been developed so far. Here we introduce a new measure of SVO that has several advantages over existent methods. A detailed description of the new measurement method is presented, along with norming data that provide evidence of its solid psychometric properties. We conclude with a brief discussion of the research streams that would benefit from a more sensitive and higher resolution measure of SVO, and extend an invitation to others to use this new measure which is freely available
|Stakeholder dependencies in recreation and tourism policy arenas: a transactional approach
Philipsen, J.F.B. ; Timmermans, J.S. - \ 2001
In: Proceedings of group decision & negotiation 2001, La Rochelle, France, 2001 / Fran Ackermann and Gert-Jan de Vreede (eds) - p. 215 - 223.
|Agro-ecological zones and their impact on farm production and farm organization after privatization in Azerbaijan
Abilejov, N. ; Krische, S. ; Wesseler, J. - \ 2001
In: Approaching Agricultural Technology and Economic Development of Central and Eastern Europe, Halle (Saale), 2001 / M. Plöchl, U. Fiege, I. Ackermann. - Potsdam-Bornim : Institute of Agricultural Engineering Bornim e.V., 2001. (Bornimer Agrartechnische Berichte ; Heft 27). - ISSN 0947-7314 - p. 37 - 42.
|Time-activity patterns in air pollution epidemiology.
Brunekreef, B. ; Hoek, G. ; Janssen, N. - \ 1995
In: Time-activity patterns in exposure assessment, Rprt 6 in 'Air Pollut. Epidemiol. Reports Series' / Ackermann-Liebrich, U., - p. 3 - 6.
|Update and revision of the air quality guidelines for Europe.
Ackermann-Liebrich, U. ; Brunekreef, B. ; Dockery, D.W. ; Folinsbee, L.J. ; Graham, J.A. ; Grant, L.D. - \ 1995
In: WHO EUR/ICP/EHAZ 9405, Meeting of the working group 'Classical' Air Pollutants, Bilthoven (1995) 25 pp
|P2:29 Cost 613/II International collaboration in air pollution epidemiology.
Ackermann-Liebrich, U.A. ; Jantunen, M.J. ; Brunekreef, B. ; Katsouyanni, K. ; Williams, M.L. ; Clancy, L. - \ 1993
In: Proc. 5th Annual Meeting Int. Soc. Environ. Epidemiol., Stockholm - p. 147 - 147.
Katsouyanni, K. ; Ackermann-Liebrich, U. ; Anderson, H.R. ; Braun-Fahrlander, C. ; Brunekreef, B. - \ 1993
Unknown Publisher - 166 p.