Thematische Bibliographien / Multidimensional scaling

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Multidimensional scaling“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "Multidimensional scaling"

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Gower, J. C., F. Cox, and M. A. A. Cox. "Multidimensional Scaling." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 184. http://dx.doi.org/10.2307/2983485.

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Jeffers, J. N. R., and Mark L. Davison. "Multidimensional Scaling." Statistician 34, no. 2 (1985): 257. http://dx.doi.org/10.2307/2988171.

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Lee, In-Soon. "Multidimensional Scaling." Journal of Korean Medical Library Association 19, no. 1 (1992): 1–6. http://dx.doi.org/10.69528/jkmla.1992.19.1.1.

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Jolliffe, Ian. "Multidimensional Scaling." Technometrics 38, no. 4 (1996): 403–4. http://dx.doi.org/10.1080/00401706.1996.10484556.

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Mugavin, Marie E. "Multidimensional Scaling." Nursing Research 57, no. 1 (2008): 64–68. http://dx.doi.org/10.1097/01.nnr.0000280659.88760.7c.

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Hout, Michael C., Megan H. Papesh, and Stephen D. Goldinger. "Multidimensional scaling." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 1 (2012): 93–103. http://dx.doi.org/10.1002/wcs.1203.

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Aflalo, Y., and R. Kimmel. "Spectral multidimensional scaling." Proceedings of the National Academy of Sciences 110, no. 45 (2013): 18052–57. http://dx.doi.org/10.1073/pnas.1308708110.

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Venna, Jarkko, and Samuel Kaski. "Local multidimensional scaling." Neural Networks 19, no. 6-7 (2006): 889–99. http://dx.doi.org/10.1016/j.neunet.2006.05.014.

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Spence, Ian, and Stephan Lewandowsky. "Robust multidimensional scaling." Psychometrika 54, no. 3 (1989): 501–13. http://dx.doi.org/10.1007/bf02294632.

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de Leeuw, Jan, and Patrick J. F. Groenen. "Inverse Multidimensional Scaling." Journal of Classification 14, no. 1 (1997): 3–21. http://dx.doi.org/10.1007/s003579900001.

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Dissertationen zum Thema "Multidimensional scaling"

1

Bell, Paul W. "Statistical inference for multidimensional scaling." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327197.

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CUGNATA, FEDERICA. "Bayesian three-way multidimensional scaling." Doctoral thesis, Università Bocconi, 2012. https://hdl.handle.net/11565/4054285.

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Jones, Synthia S. "Multidimensional scaling of user information satisfaction." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA277230.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, December 1993.<br>Thesis advisor(s): William J. Haga ; Kishore Sengupta. "December 1993." Bibliography: p. 108-110. Also available online.
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Ingram, Stephen F. "Multilevel multidimensional scaling on the GPU." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/409.

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We present Glimmer, a new multilevel visualization algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hard-ware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm helps avoid local minima while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demo
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McQuaid, Michael J., Thian-Huat Ong, Hsinchun Chen, and Jay F. Nunamaker. "Multidimensional scaling for group memory visualization." Elsevier, 1999. http://hdl.handle.net/10150/105458.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>We describe an attempt to overcome information overload through information visualization â in a particular domain, group memory. A brief review of information visualization is followed by a brief description of our methodology. We . discuss our system, which uses multidimensional scaling MDS to visualize relationships between documents, and which . we tested on 60 subjects, mostly students. We found three important and statistically significant differences between task performance on an MDS-generated display and
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Tulabandula, Sridhar. "Localization of wireless sensor networks using multidimensional scaling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4986.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on April 17, 2008) Includes bibliographical references.
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Sun, Jiang. "Extending the metric multidimensional scaling with bregman divergences." Thesis, University of the West of Scotland, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556070.

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Mohd, Yunus Mohd Yusri. "Multivariate statistical process monitoring using classical multidimensional scaling." Thesis, University of Newcastle upon Tyne, 2012. http://hdl.handle.net/10443/1495.

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A new Multivariate Statistical Process Monitoring (MSPM) system, which comprises of three main frameworks, is proposed where the system utilizes Classical Multidimensional Scaling (CMDS) as the main multivariate data compression technique instead of using the linearbased Principal Component Analysis (PCA). The conventional method which usually applies variance-covariance or correlation measure in developing the multivariate scores is found to be inappropriately used especially in modelling nonlinear processes, where a high number of principal components will be typically required. Alternativel
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Williams, Michelle A. "A factor structure with means confirmatory factor analytic approach to multitrait-multimethod models." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5010.

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Thesis (M.A.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 6, 2007) Includes bibliographical references.
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Jansson, Mattias, and Jimmy Johansson. "Interactive Visualization of Statistical Data using Multidimensional Scaling Techniques." Thesis, Linköping University, Department of Science and Technology, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1716.

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<p>This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137. </p><p>In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions. </p><p>In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As
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Bücher zum Thema "Multidimensional scaling"

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A, Cox Michael A., ed. Multidimensional scaling. 2nd ed. Chapman & Hall/CRC, 2001.

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Davison, Mark L. Multidimensional scaling. Krieger Pub. Co., 1992.

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Borg, Ingwer, Patrick J. F. Groenen, and Patrick Mair. Applied Multidimensional Scaling. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31848-1.

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Borg, Ingwer, and Patrick Groenen. Modern Multidimensional Scaling. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2711-1.

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F, Groenen Patrick J., Mair Patrick, and SpringerLink (Online service), eds. Applied Multidimensional Scaling. Springer Berlin Heidelberg, 2013.

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Biela, Adam. Skalowanie wielowymiarowe jako metoda badań naukowych. Tow. Naukowe Katolickiego Uniwersytetu Lubelskiego, 1992.

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Schubert, Leo. Lösungsansätze der mehrdimensionalen Skalierung mit Berücksichtigung unterschiedlicher Datenniveaus. A. Hain, 1985.

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Borg, Ingwer, Patrick J. F. Groenen, and Patrick Mair. Applied Multidimensional Scaling and Unfolding. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73471-2.

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J, Carmone Frank, and Smith Scott M, eds. Multidimensional scaling: Concepts and applications. Allyn and Bacon, 1989.

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Green, Paul E. Multidimensional scaling: Concepts and applications. Allyn and Bacon, 1989.

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Buchteile zum Thema "Multidimensional scaling"

1

Härdle, Wolfgang Karl, and Zdeněk Hlávka. "Multidimensional Scaling." In Multivariate Statistics. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_17.

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Härdle, Wolfgang, and Léopold Simar. "Multidimensional Scaling." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_15.

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Everitt, Brian S., and Graham Dunn. "Multidimensional Scaling." In Applied Multivariate Data Analysis. John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch5.

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Shen, Heng Tao. "Multidimensional Scaling." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_548-2.

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du Toit, S. H. C., A. G. W. Steyn, and R. H. Stumpf. "Multidimensional Scaling." In Springer Texts in Statistics. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4950-4_6.

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Härdle, Wolfgang Karl, and Léopold Simar. "Multidimensional Scaling." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_17.

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Mukherjee, S. P., Bikas K. Sinha, and Asis Kumar Chattopadhyay. "Multidimensional Scaling." In Statistical Methods in Social Science Research. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2146-7_11.

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Adachi, Kohei. "Multidimensional Scaling." In Matrix-Based Introduction to Multivariate Data Analysis. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_16.

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Härdle, Wolfgang Karl, and Léopold Simar. "Multidimensional Scaling." In Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_16.

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Jacoby, William G., and David J. Ciuk. "Multidimensional Scaling." In The Wiley Handbook of Psychometric Testing. John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781118489772.ch14.

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Konferenzberichte zum Thema "Multidimensional scaling"

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Dennig, Frederik L., Lucas Joos, Patrick Paetzold, et al. "The Categorical Data Map: A Multidimensional Scaling-Based Approach." In 2024 IEEE Visualization in Data Science (VDS). IEEE, 2024. http://dx.doi.org/10.1109/vds63897.2024.00008.

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Zhang, Che, and Peng Han. "Multidimensional Scaling and Subnet Stitching for Enhanced Cooperative Localization." In 2025 6th International Conference on Computer Science, Engineering, and Education (CSEE). IEEE, 2025. https://doi.org/10.1109/csee64583.2025.00015.

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Mackute-Varoneckiene, Ausra, Antanas Zilinskas, and Audrius Varoneckas. "Multidimensional scaling." In the International Conference. ACM Press, 2009. http://dx.doi.org/10.1145/1731740.1731805.

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Runkler, Thomas A., and James C. Bezdek. "Multidimensional scaling with multiswarming." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900225.

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Zhang, Xi, Hao Huang, Klaus Mueller, and Shinjae Yoo. "Streaming Classical Multidimensional Scaling." In 2018 New York Scientific Data Summit (NYSDS). IEEE, 2018. http://dx.doi.org/10.1109/nysds.2018.8538942.

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Kumar, Sandeep, and Ketan Rajawat. "Velocity-assisted multidimensional scaling." In 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015. http://dx.doi.org/10.1109/spawc.2015.7227102.

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Di Franco, Carmelo, Enrico Bini, Mauro Marinoni, and Giorgio C. Buttazzo. "Multidimensional scaling localization with anchors." In 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, 2017. http://dx.doi.org/10.1109/icarsc.2017.7964051.

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Vu, Viet Minh, Adrien Bibal, and Benoit Frenay. "iPMDS: Interactive Probabilistic Multidimensional Scaling." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534425.

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Qu, Taiguo, and Zixing Cai. "A fast multidimensional scaling algorithm." In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015. http://dx.doi.org/10.1109/robio.2015.7419726.

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Qu, Taiguo, and Dong Wang. "A Fast Multidimensional Scaling Algorithm." In ICEITSA 2023: The 3rd International Conference on Electronic Information Technology and Smart Agriculture. ACM, 2023. http://dx.doi.org/10.1145/3641343.3641431.

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Berichte der Organisationen zum Thema "Multidimensional scaling"

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Cvetkovski, Andrej, and Mark Crovella. Multidimensional Scaling in the Poincare Disk. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada585960.

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Oh, Man-Suk, and Adrian E. Raftery. Bayesian Multidimensional Scaling and Choice of Dimension. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada458817.

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Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset. The Solution of the Metric STRESS and SSTRESS Problems in Multidimensional Scaling Using Newton's Method. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada445621.

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Koven, Charles, R. Knox, R. Fisher, et al. Modular hybrid modeling to increase efficiency, explore structural uncertainty, andallow multidimensional complexity scaling in land surface models. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769750.

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Whitecloud, Simone, Kevin Philley, J. Minkin, et al. Assessing the Validity and Accuracy of Wetland Indicator Status Ratings for Eight Species in Alaska Subregions. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49509.

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Preexisting ecological information and plant species occurrence data were used to determine the accuracy and validity of the present regional and subregional wetland indicator status ratings for eight species: Andromeda polifolia, Arctous rubra, Carex canescens, Rhododendron tomentosum, Rubus arcticus, Salix arctica, Salix pulchra, and Viola palustris. Technical documentation was developed to either (1) support the current National Wetland Plant List (NWPL) subregion boundaries and wetland indicator status ratings for the NWPL Alaska Region or (2) support a proposed change to the subregions or
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Coscia, Jordan, Wendy Cass, and Leighton J. Evaluation of 20 years of vegetation change in the Big Meadows landscape of Shenandoah National Park in response to mowing and burning: Application of historic data to identify best practices for future meadow management. National Park Service, 2025. https://doi.org/10.36967/2312599.

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The Big Meadows landscape is an open habitat that supports unique biodiversity not found elsewhere in Shenandoah National Park. To support this biodiversity and to maintain the Meadow’s historic landscape characteristics, Park staff have managed the meadows using prescribed burns, mowing, and the targeted cutting and removal of woody plants since 1998. To assess the impacts of these management actions on the plant community in Big Meadows and to guide future land management recommendations, we visualized changes in the plant community composition between 1998 and 2022 using non-metric multidim
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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner, and Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2284511.

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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2
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Nikula, Blair, and Robert Cook. Status and distribution of Odonates at Cape Cod National Seashore. National Park Service, 2024. http://dx.doi.org/10.36967/2303254.

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Odonates are significant components of most wetland habitats and important indicators of their health. At Cape Cod National Seashore (CACO), we compiled odonate records dating back to the 1980s and, based partly on that data, identified 41 wetland sites for sampling, representing six freshwater habitats (kettle pond, inter-dune pond, dune slack, riparian marsh, vernal pool, and bog). We surveyed these sites for adult odonates during the 2016?2018 field seasons. Ten sites were surveyed all three years (total 19-20 surveys/site); all ten had at least some historical data. The remaining 31 sites
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