Recent research has also highlighted concerns about the possible risks posed by mobile phone data in relation to data gathered via traditional census means; if the census outputs are published at a high level of spatial detail. A mobile phone data set with person identifiers might act as a key to census data, leading to attribute disclosure. Whilst it is reasonably easy to protect census data (at the cost of user access) this example indicates the tensions between administrative census collection and traditional forms (whether they be collected on paper or online).
The existence – or potential existence – of a census-like spain rcs data data set (in the form of administrative data and big data) places new constraints on the ways in which data can be made available from traditional censuses, at odds with increasing expectations of open data. addressed with both open data and (more detailed) restricted-access versions, there must be a risk that it becomes easier to simply not release the more detailed data.
On a more general level one may ask whether it is actually possible to quantify impact. For example, if an aspiring PhD candidate uses one article as the backdrop and inspiration of his, or her, PhD, does this mean that the article has had less impact than if it had been cited by 10 different scholars?
Scholars generally acknowledge that metrics do have flaws, but still generally trust them for reading and publishing (Tenopir, 2014).