Value proposition

Value is usually created by the usefulness of the object, which in turn depends on its usability. However there are many other reasons for assigning value for example having local control over critical pieces of information even if no use is expected in the foreseeable future.

The value may be “potential” value – in that there is no certainty in that value and perhaps some evaluation period would be needed or one might need to create an “option of having the assets available for as-yet-unknown uses that may emerge in the future” [BRTF].

An important aspect of the value is an estimate of resources which might be attracted, for example commercial payments for use or advertising revenue or academic value; alternatively the value may be in terms of penalties which might be avoided, for example legal penalties or the costs of replacing the objects if lost.

The object may be of value because it cannot be re-created (for example evidence of Climate change) or because of the cost of (re-)creation of the object (for example the data gathered by the LHC).

The object will probably be more useful to one type of user community than to another, and this may change over time.

Rights may be associated with the objects, perhaps arising from the value or potential value of the object.

Asset base

Issue WP/Project/Tools/Services Asset Evidence

How to assign value to digital objects

BRTF report

[Download not found]

LIFE project

LIFE tools

DP Impact

Calculating the Value of Digital Objects at Risk

Tessella DVAR

DVAR calculator

Value vs. legal mandate as main driver for implementing DP practices


DP Impact

[Download not found] DP preparedness in Scientific libraries.

[Download not found] Exemplary business cases

DPImpact chapters 4 & 5

Description of currently in place policies and practices in pioneering DP practices

In terms of cost models, may be covered by impact or benefits assessments which link to value. See KRDS model.

APARSEN WP32 cost modelling

Further assessment of [Download not found] and [Download not found] could be undertaken to provide training, consultancy – not strictly although could be considered a service perhaps, but we could provide guidance and advice on best tools available given specific situations to assess value of DP. This would, however, need to be investigated further. In terms of tools, we could refer to specific models which are currently available.

Identifiers as enablers of value in scientific data e-infrastructures (SDIs) in particular for developing value-added services on top of scientific data and contents. These services deal with many aspects of the e-science landscape including data and information access, knowledge discovery, Citability, quality assessment and provenance.



DIGOIDUNA final report, in particular SWOT analysis and recommendations

ODIN deliverables available at

Preliminary models and results on data exchange improvements and workflows

Results will be published soon at



The major gaps are related to the identification, classification and quantification of the benefits and impacts the usage of preserved objects may produce. In turn this also requires the identification of the targeted users of the preserved objects (e.g. researchers or students) and the customers/purchasers of such collections (e.g. companies or universities)Very limited work done on value in terms of cost models.
Persistent identifiers. 1) A common agenda among key stakeholders towards the design and implementation of a governance model and an integrating infrastructure for managing PIs in SDIs in which technological, economic, social and political factors are taken into account; 2) Common policies on the governance of PIs and integrating technical solutions; 3) Mobilization of technical, human and financial resources to trigger a wider demand of usage and exploitation of e-Science results based on PIs. 4) Suitable business models and organizational mechanisms to ensure long-term sustainability of the implemented solutions.Lack of interoperability between identification systems for data on one hand and for contributors on the other hand. This can cause 1) the inability to follow interconnections between datasets and contributors as a method for data discovery; 2) the inability to share and connect identifiers of contributors and authors between different user communities; 3) inability to uniquely identify datasets attributed to a particular contributor and contributors to a particular dataset.