The advantages of an Ontology-Based Data Management approach: openness, interoperability and data quality

Daraio, Cinzia; Lenzerini, Maurizio; Leporelli, Claudio; Naggar, Paolo; Bonaccorsi, Andrea; Bartolucci, Alessandro

doi:10.1007/s11192-016-1913-6

Cited by 68 publications

(29 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such constituents of data quality as provenance and integrity, as well as the accuracy, consistency, completeness, originality and timeliness of data sources should play a central role in data literacy education [36]. By being largely standardised, also data governance is applicable to teaching data literacy [37].…”

Section: Data Literacymentioning

confidence: 99%

Identifying new roles for academic libraries in supporting data-intensive research

Koltai

2020

Библиосфера

View full text Add to dashboard Cite

Reacting to the appearance of data-intensive research prompts academic libraries to become service providers for scholars, who work with research data. Although this is an imperative for libraries worldwide, due to the differences between countries and institutions, the level of readiness to engage in related activities differs from country to country. While some of the related tasks are fairly novel, others heavily build on librarians’ traditional, well-known skills. To identify these tasks, as well as making an inventory of the required skills and abilities, this paper, based on a non-exhaustive review of the recent literature, presents both theoretical and practical issues. It is demonstrated that the most obvious directions of the service development in academic libraries to support data-intensive science are research data management, data curation, data literacy education for users, and data literacy education for librarians.

show abstract

Section: Data Literacymentioning

confidence: 99%

Identifying new roles for academic libraries in supporting data-intensive research

Koltai

2020

Библиосфера

View full text Add to dashboard Cite

show abstract

“…Daraio and Bonaccorsi () illustrate the design of a possible information system to integrate microdata on universities with other sources with the aim of overcoming rankings by linking data in an open platform. In particular, the new developments may be useful to consider data quality dimensions and the openness of the data platform, although the limits of data and their availability and interoperability still remain a critical issue (Borgman, ; Daraio, Lenzerini, Leporelli, Naggar, et al, ) that need to be considered in the development of metrics for the assessment of research, education and innovation (Daraio, ).…”

Section: Research and Higher Education In Today's Societymentioning

confidence: 99%

Policy turnaround: Towards a new deal for research and higher education. Governance, evaluation and rankings in the big data era

Daraio

Heitor²,

Meoli

et al. 2019

Higher Education Quarterly

Self Cite

View full text Add to dashboard Cite

“…Figure 2 shows the role of our modeling framework in its interplay with the empirical and policy world for the understanding of the real world. We claim that the more the Quality is conceptually and formally specified, the more the overlapping  Within some research projects funded by Sapienza University in 2013 and 2015 we did an experiment of a knowledge infrastructure, a case of an "open science of science" exercise, around Sapientia: The Ontology of Multi-Dimensional Research Assessment (Daraio et al, 2016a;2016b). Sapientia represents an effort of going toward a common platform which can show which data has to be collected; by offering the opportunity of making analysis under different perspectives, testing different models, but sharing the same common conceptual characterization.…”

Section: Toward Responsible Metrics?mentioning

confidence: 99%

A Framework for the Assessment of Research and Its Impacts

Daraio

2017

Journal of Data and Information Science

Self Cite

View full text Add to dashboard Cite

This paper proposes a holistic framework for the development of models for the assessment of research activities and their impacts. It distinguishes three dimensions, including in an original way, data as a main dimension, together with theory and methodology. Each dimension of the framework is further characterized by three main building blocks: education, research, and innovation (theory); efficiency, effectiveness, and impact (methodology); and availability, interoperability, and “unit-free” property (data). The different dimensions and their nine constituent building blocks are attributes of an overarching concept, denoted as “quality.” Three additional quality attributes are identified as implementation factors (tailorability, transparency, and openness) and three “enabling” conditions (convergence, mixed methods, and knowledge infrastructures) complete the framework. A framework is required to develop models of metrics. Models of metrics are necessary to assess the meaning, validity, and robustness of metrics. The proposed framework can be a useful reference for the development of the ethics of research evaluation. It can act as a common denominator for different analytical levels and relevant aspects and is able to embrace many different and heterogeneous streams of literature. Directions for future research are provided.

show abstract

The advantages of an Ontology-Based Data Management approach: openness, interoperability and data quality

Cited by 68 publications

References 18 publications

Identifying new roles for academic libraries in supporting data-intensive research

Identifying new roles for academic libraries in supporting data-intensive research

Policy turnaround: Towards a new deal for research and higher education. Governance, evaluation and rankings in the big data era

A Framework for the Assessment of Research and Its Impacts

Contact Info

Product

Resources

About