Specimens as Research Objects: Reconciliation across Distributed Repositories to Enable Metadata Propagation
Nicky Nicolson
Alan Paton
Sarah Phillips
Allan Tucker
10.6084/m9.figshare.7327325.v1
https://figshare.com/articles/presentation/Specimens_as_Research_Objects_Reconciliation_across_Distributed_Repositories_to_Enable_Metadata_Propagation/7327325
<div>Slides from presentation at IEEE eScience 2018. Links to access the full paper given in the references section.</div><div><br></div><div><b>Abstract</b></div>Botanical specimens are shared as long-term consultable
research objects in a global network of specimen repositories.
Multiple specimens are generated from a shared field
collection event; generated specimens are then managed individually
in separate repositories and independently augmented with
research and management metadata which could be propagated
to their duplicate peers. Establishing a data-derived network for
metadata propagation will enable the reconciliation of closely
related specimens which are currently dispersed, unconnected
and managed independently. Following a data mining exercise
applied to an aggregated dataset of 19,827,998 specimen records
from 292 separate specimen repositories, 36% or 7,102,710
specimens are assessed to participate in duplication relationships,
allowing the propagation of metadata among the participants
in these relationships, totalling: 93,044 type citations, 1,121,865
georeferences, 1,097,168 images and 2,191,179 scientific name
determinations. The results enable the creation of networks to
identify which repositories could work in collaboration. Some
classes of annotation (particularly those regarding scientific name
determinations) represent units of scientific work: appropriate
management of this data would allow the accumulation of
scholarly credit to individual researchers: potential further work
in this area is discussed.<div><br></div>
2018-11-13 11:09:19
Research Objects
data citation
record linkage
annotation
Botany
Applied Computer Science
Pattern Recognition and Data Mining