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  • 2020-2022  (3)
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  • 1
    Publication Date: 2020-05-17
    Description: Germany’s national ocean observing activities are carried out by multiple actors including governmental bodies, research institutions, and universities, and miss central coordination and governance. A particular strategic approach to coordinate and facilitate ocean research has formed in Germany under the umbrella of the German Marine Research Consortium (KDM). KDM aims at bringing together the marine science expertise of its member institutions and collectively presents them to policy makers, research funding organizations, and to the general public. Within KDM, several strategic groups (SGs), composed of national experts, have been established in order to strengthen different scientific and technological aspects of German Marine Research. Here we present the SG for sustained open ocean observing and the SG for sustained coastal observing. The coordination effort of the SG’s include (1) Representing German efforts in ocean observations, providing information about past, ongoing and planned activities and forwarding meta-information to data centers (e.g., JCOMMOPS), (2) Facilitating the integration of national observations into European and international observing programs (e.g. GCOS, GOOS, BluePlanet, GEOSS), (3) Supporting innovation in observing techniques and the development of scientific topics on observing strategies, (4) Developing strategies to expand and optimize national observing systems in consideration of the needs of stakeholders and conventions, (5) Contributing to agenda processes and roadmaps in science strategy and funding, and (6) Compiling recommendations for improved data collection and data handling, to better connect to the global data centers adhering to quality standards.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
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  • 2
    Publication Date: 2021-09-28
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 3
    Publication Date: 2021-12-01
    Description: Species identification using matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) data strongly relies on reference libraries to differentiate species. Because comprehensive reference libraries, especially for metazoans, are rare, we explored the accuracy of unsupervised diversity estimations of communities using MALDI-TOF MS data in the absence of reference libraries to provide a method for future application in ecological research. To discover the best analysis strategy providing high congruence with true community structures, we carried out a simulation with more than 30,000 analyses using different combinations of data transformations, dimensionality reductions, and cluster algorithms. Species profile, Hellinger, and presence/absence transformations were applied to raw data and dimensions were reduced using principal component analysis (PCA), t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. To estimate biodiversity, data were clustered making use of partitioning around medoids, model-based clustering, and K-means clustering. The analyses were carried out on published mass spectrometry data of harpacticoid copepods. Most successful combinations (Hellinger transformation + PCA or raw data + partitioning around medoids) returned good values even for difficult species distributions containing numerous singleton species. Nevertheless, errors occurred most frequently because of such singleton taxa. Hence, replicative sampling in wide sampling areas for analysis is emphasized to increase the minimum number of specimens per species, thus reducing putative sources of errors. Our results demonstrate that MALDI-TOF MS data can be used to accurately estimate the biodiversity of unknown communities using unsupervised learning methods. The provided approach allows the biodiversity comparison of sampled regions for which no reference libraries are available. Hence, especially data on groups which demand a time-consuming identification or are highly abundant can be analyzed within short working time, accelerating ecological studies.
    Keywords: 577 ; biodiversity estimation ; metazoans ; methods
    Language: English
    Type: map
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