This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of he...
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Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expan...
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Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.
The DASUD (Diffusion Algorithm Searching Unbalanced Domains) algorithm belongs to the nearest-neighbours class and operates in a diffusion scheme where a processor balances its load with all its neighbours. DASUD dete...
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The DASUD (Diffusion Algorithm Searching Unbalanced Domains) algorithm belongs to the nearest-neighbours class and operates in a diffusion scheme where a processor balances its load with all its neighbours. DASUD detects unbalanced domains and performs local exchange of load between processors to achieve global balancing. The DASUD algorithm has been evaluated by comparison with another well-known strategy, namely, the SID (Sender Initiated Diffusion) algorithm across a range of network topologies including ring, torus and hypercube where the number of processors varies from 8 to 128. From the experiments we have observed that DASUD outperforms the other strategy as it provides the best trade-of-between the balance degree obtained at the final state and the number of iterations required to reach such a state. DASUD is able to coerce any initial load distribution into a highly balanced global state and also exhibits good scalability properties.
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