Estimating open access mandate effectiveness: The <scp>MELIBEA</scp> score is a dataset published in Journal of the Association for Information Science and Technology (2015). On theSindex it has a DataRank of 4.3, placing it in the top 29.5% of the data-sharing corpus. It has been cited 49 times, with 45 citing works in its 1-hop citation network.
MELIBEA is a directory of institutional open‐access policies for research output that uses a composite formula with eight weighted conditions to estimate the “strength” of open access (OA) mandates (registered in ROARMAP). We analyzed total Web of Science‐(WoS)‐indexed publication output in years 2011–2013 for 67 institutions in which OA was mandated to estimate the mandates' effectiveness: How well did the MELIBEA score and its individual conditions predict what percentage of the WoS‐indexed articles is actually deposited in each institution's OA repository, and when? We found a small but significant positive correlation (0.18) between the MELIBEA “strength” score and deposit percentage. For three of the eight MELIBEA conditions (deposit timing, internal use, and opt‐outs), one value of each was strongly associated with deposit percentage or latency ([a] immediate deposit required; [b] deposit required for performance evaluation; [c] unconditional opt‐out allowed for the OA requirement but no opt‐out for deposit requirement). When we updated the initial values and weights of the MELIBEA formula to reflect the empirical association we had found, the score's predictive power for mandate effectiveness doubled (0.36). There are not yet enough OA mandates to test further mandate conditions that might contribute to mandate effectiveness, but the present findings already suggest that it would be productive for existing and future mandates to adopt the three identified conditions so as to maximize their effectiveness, and thereby the growth of OA.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.584
From this paper's citation signal
Citation Network Contribution
3.8
From 38 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 13% comes from its base citations and 87% from the citation network (38 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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