Metagenome-assembled genomes provide new insight into the microbial diversity of two thermal pools in Kamchatka, Russia is a dataset published in Scientific Reports (2019). On theSindex it has a DataRank of 2.7, placing it in the top 33.2% of the data-sharing corpus. It has been cited 108 times, with 115 citing works in its 1-hop citation network. Its calibrated FAIR score is 53/100.
Culture-independent methods have contributed substantially to our understanding of global microbial diversity. Recently developed algorithms to construct whole genomes from environmental samples have further refined, corrected and revolutionized understanding of the tree of life. Here, we assembled draft metagenome-assembled genomes (MAGs) from environmental DNA extracted from two hot springs within an active volcanic ecosystem on the Kamchatka peninsula, Russia. This hydrothermal system has been intensively studied previously with regard to geochemistry, chemoautotrophy, microbial isolation, and microbial diversity. We assembled genomes of bacteria and archaea using DNA that had previously been characterized via 16S rRNA gene clone libraries. We recovered 36 MAGs, 29 of medium to high quality, and inferred their placement in a phylogenetic tree consisting of 3,240 publicly available microbial genomes. We highlight MAGs that were taxonomically assigned to groups previously underrepresented in available genome data. This includes several archaea (Korarchaeota, Bathyarchaeota and Aciduliprofundum) and one potentially new species within the bacterial genus Sulfurihydrogenibium. Putative functions in both pools were compared and are discussed in the context of their diverging geochemistry. This study adds comprehensive information about phylogenetic diversity and functional potential within two hot springs in the caldera of Kamchatka.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.702
From this paper's citation signal
Citation Network Contribution
2.0
From 74 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 26% comes from its base citations and 74% from the citation network (74 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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