TeSS: a platform for discovering life-science training opportunities is a dataset published in Bioinformatics (2020). On theSindex it has a DataRank of 0.948, placing it in the top 42.2% of the data-sharing corpus. It has been cited 26 times, with 16 citing works in its 1-hop citation network. Its calibrated FAIR score is 51/100.
SummaryDispersed across the Internet is an abundance of disparate, disconnected training information, making it hard for researchers to find training opportunities that are relevant to them. To address this issue, we have developed a new platform-TeSS-which aggregates geographically distributed information and presents it in a central, feature-rich portal. Data are gathered automatically from content providers via bespoke scripts. These resources are cross-linked with related data and tools registries, and made available via a search interface, a data API and through widgets.Availability and implementationhttps://tess.elixir-europe.org.
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
0.489
From this paper's citation signal
Citation Network Contribution
0.460
From 12 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 52% comes from its base citations and 48% from the citation network (12 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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