In addition, current data from E&H monitoring programmes face many challenges: (i) fragmentation of datasets and sources; (ii) lack of harmonization between datasets at different geographical scales [ 90], and (iii) issues of data quality and accuracy.
The AMAP identified data access and sharing as key areas requiring improvement [ 34]; (iii) lack of harmonization between datasets.
To cope with these differences, two approaches are possible: centralized analyses, which include harmonization of the datasets to create one large dataset in which the analyses are done, or coordinated independent analyses, in which the analyses are done in each dataset and the results per dataset are pooled afterwards [ 13].
Importantly, the benefits of protocols and systems for systematically combining datasets, pairing variables and applying harmonization strategies have been explored previously [ 46].
Starting from the frameworks of existing international datasets and data models also will enable the incremental enhancement of these frameworks to enable harmonization of additional open datasets among different jurisdictions and organisations.
Table 1 includes summary statistics of the control variables after the harmonization between the two datasets.
The steps cover (i) legacy data identification and collection, (ii) data selection, (iii) database development and population, (iv) data harmonization and display, and (v) dataset integration.
(PDF 15 KB) 40176_2014_49_MOESM2_ESM.pdf Additional file 2: Table 8: Variable harmonization across GSS and HILDA datasets.
Second, further standardization and harmonization of existing land use datasets as well as data collection protocols are needed, similar to efforts focusing on land cover (e.g. [ 57]).
For harmonizing datasets, it is recommended to consult a(n) (external) harmonization expert, who is familiar with Source and Target data structures.
