List of plant species found on Victoria Island

While engaged in geoecological field work on Victoria Island, 277 new plants could be recorded for the vicinities of Holman, Cambridge Bay, Wellington Bay, Mt. Pelly, Richardson Islands, Hadley Bay, and Minto lnlet; 8 of them were new for Victoria Island, 6 for the western Canadian arctic archipelago.

Species list of the flora on Banks Island

While engaged in palaeo-botanical and plantgeographical field work on Banks Island, N. W. T. during the summer of 1973, 225 new plants could be recorded for the vicinities of Sachs Harbour, Shoran Lake and Johnson Point; 21 of them were new for Banks Island, 7 for the western Canadian archipelago.

Appendix 1: List of cladistic characters published on ammonites

The file here provided, is the list of all characters that have been used in cladistic analysis on ammonoids published so far. It constitutes the base of a study which investigates practices in characters establishment. Find here after the abstract of the article that is associated to this file. Cladistics appears as one of the most useful method to reconstruct phylogeny of fossil taxa. However, ammonoids workers tend to sulk this method. The capital step of cladistic analysis is the recognition of homology hypothesis as clue to reconstruct monophyletic clades based on the sharing of derived traits. Previous authors have suggested that coding schemes are usually direct transcription of original taxa description. However, establishing a list of characters (i.e. a matrix taxa /characters) is a very different work compared to a compilation of diagnoses. How morphology is coded in ammonoids? How coding schemes are influenced by traditional descriptions / characters? Here, we review all cladistic analyses of ammonoids published in the literature to compare characters and the way authors have dealt with the treatment of continuous characters, polymorphism and ontogeny. Several barriers are usually invoked to justify that cladistics cannot be applied to reconstruct ammonoids phylogenies. We show that an appropriate use of improvements both on ammonoids' knowledge and cladistics methodology may overcome limitations usually invoked to perform cladistic analysis on ammonoids.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.