For some decades now, Galleries, Libraries, Archives and Museums (GLAM) institutions have published and provided access to information resources in digital format. Recently, innovative approaches have appeared such as the concept of Labs within GLAM institutions that facilitates the adoption of innovative and creative tools for content delivery and user engagement. In addition, new methods have been proposed to address the publication of digital collections as data sets amenable to computational use. In this article, we propose a methodology to create machine actionable collections following a set of steps. This methodology is then applied to several use cases based on data sets published by relevant GLAM institutions. It intends to encourage institutions to adopt the publication of data sets that support computationally driven research as a core activity.
1 February 2020 in Computer Standards & Interfaces
Nowadays, the increasing demand of water for electricity production, agricultural and industrial uses are directly affecting the reduction of available quality water for human consumption in the world. Efficient and sustainable maintenance of water reservoirs and supply networks implies a holistic strategy that takes into account, as much as possible, information from the stages of water usage. Next,-generation decision-making software tools, for supporting water management, require the integration of multiple and heterogeneous data sources of different knowledge domains. In this regard, Linked Data and Semantic Web technologies enable harmonization of different data sources, as well as the efficient querying for feeding upper-level Business Intelligence processes. This work investigates the design, implementation and usage of a semantic approach driven by ontology to capture, store, integrate and exploit real-world data concerning water supply networks management. As a main contribution, the proposal helps with obtaining semantically enriched linked data, enhancing the analysis of water network performance. For validation purposes, in the use case, a series of data sources from different measures have been considered, in the scope of an actual water management system of the Mediterranean region of Valencia (Spain), throughout several years of activity. The obtained experience shows the benefits of using the proposed approach to identify possible correlations between the measures such as the supplied water, the water leaks or the population.
16 November 2023 in Journal on Computing and Cultural Heritage
Cultural Heritage institutions have been exploring new ways of making available their catalogues in digital format. Recently, new approaches have emerged as methods to reuse and make available the contents for computational purposes. This work introduces a methodology to transform digital collections into Linked Open Data following best practices. The framework has been applied to Indigenous and Spanish colonial archives based on the collection Relaciones Geográficas of Mexico and Guatemala provided by the LLILAS Benson Latin American Studies and Collections. The results of this work are publicly available. This work aims at encouraging Cultural Heritage institutions to publish and reuse their digital collections using advanced methods and techniques.
13 December 2021 in Journal of Information Science
In the domain of Galleries, Libraries, Archives and Museums (GLAM) institutions, creative and innovative tools and methodologies for content delivery and user engagement have recently gained international attention. New methods have been proposed to publish digital collections as datasets amenable to computational use. Standardised benchmarks can be useful to broaden the scope of machine-actionable collections and to promote cultural and linguistic diversity. In this article, we propose a methodology to select datasets for computationally driven research applied to Spanish text corpora. This work seeks to encourage Spanish and Latin American institutions to publish machine-actionable collections based on best practices and avoiding common mistakes.