AMDB: a database of animal gut microbial communities with manually curated metadata
AMDB: a database of animal gut microbial communities with manually curated metadata is a dataset published in Nucleic Acids Research (2021). On theSindex it has a DataRank of 1.2, placing it in the top 40.7% of the data-sharing corpus. It has been cited 24 times, with 24 citing works in its 1-hop citation network. Its calibrated FAIR score is 48/100.
Abstract
Variations in gut microbiota can be explained by animal host characteristics, including host phylogeny and diet. However, there are currently no databases that allow for easy exploration of the relationship between gut microbiota and diverse animal hosts. The Animal Microbiome Database (AMDB) is the first database to provide taxonomic profiles of the gut microbiota in various animal species. AMDB contains 2530 amplicon data from 34 projects with manually curated metadata. The total data represent 467 animal species and contain 10 478 bacterial taxa. This novel database provides information regarding gut microbiota structures and the distribution of gut bacteria in animals, with an easy-to-use interface. Interactive visualizations are also available, enabling effective investigation of the relationship between the gut microbiota and animal hosts. AMDB will contribute to a better understanding of the gut microbiota of animals. AMDB is publicly available without login requirements at http://leb.snu.ac.kr/amdb.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
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 →
DataRank Breakdown
Base Score Contribution
0.464
From this paper's citation signal
Citation Network Contribution
0.709
From 20 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- FastTree 2 – Approximately Maximum-Likelihood Trees for Large AlignmentsPLoS ONE201015,715 citationsDataRank 1.4
- VSEARCH: a versatile open source tool for metagenomicsPeerJ201610,782 citationsDataRank 1.4
- Diet rapidly and reproducibly alters the human gut microbiomeNature201310,056 citationsDataRank 1.4
- Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assembliesInternational Journal of Systematic and Evolutionary Microbiology20177,690 citationsDataRank 14.4Top 14%
- Qiita: rapid, web-enabled microbiome meta-analysisNature Methods2018697 citationsDataRank 0.982
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 40% comes from its base citations and 60% from the citation network (20 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
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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