Mapping STI ecosystems via Open Data: overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark

• URL: http://arxiv.org/abs/2209.08920v1
• Date: Mon, 19 Sep 2022 10:59:39 GMT
• Title: Mapping STI ecosystems via Open Data: overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark
• Authors: Nicandro Bovenzi, Nicolau Duran-Silva, Francesco Alessandro Massucci, Francesco Multari, C\`esar Parra-Rojas, and Josep Pujol-Llatse
• Abstract summary: Science, Technology and Innovation (STI) decision-makers often need to have a clear vision of what is researched and by whom to design effective policies. A major challenge to be faced in this context is the difficulty in accessing the relevant data and in combining information coming from different sources. Here, we present a proof-of-concept study of the use of Open Resources to map the research landscape on the Sustainable Development Goal (SDG) 13-Climate Action, for an entire country, Denmark, and we map it on the 25 ERC panels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Science, Technology and Innovation (STI) decision-makers often need to have a clear vision of what is researched and by whom to design effective policies. Such a vision is provided by effective and comprehensive mappings of the research activities carried out within their institutional boundaries. A major challenge to be faced in this context is the difficulty in accessing the relevant data and in combining information coming from different sources: indeed, traditionally, STI data has been confined within closed data sources and, when available, it is categorised with different taxonomies. Here, we present a proof-of-concept study of the use of Open Resources to map the research landscape on the Sustainable Development Goal (SDG) 13-Climate Action, for an entire country, Denmark, and we map it on the 25 ERC panels.

Related papers

• Data governance: A Critical Foundation for Data Driven Decision-Making in Operations and Supply Chains [5.909817496975273]
  This study aims to call attention on Data Governance (DG) research in the field of operations and supply chain management (OSCM) Built upon three case studies, we exanimated and analyzed real life data issues in the industry. Four types of cause related to data issues were found: 1) human factors, 2) lack of written rules and regulations, 3) ineffective technological hardware and software, and 4) lack of resources.
  arXiv  Detail & Related papers  (2024-09-23T15:41:56Z)
• Navigating the Data Trading Crossroads: An Interdisciplinary Survey [33.64953318642493]
  Data has been increasingly recognized as a critical factor in the future economy. However, constructing an efficient data trading market faces challenges such as privacy breaches, data monopolies, and misuse. This paper aims to identify existing problems, research gaps, and propose potential solutions.
  arXiv  Detail & Related papers  (2024-07-16T08:07:16Z)
• Navigating the Research Landscape of Decentralized Autonomous Organizations: A Research Note and Agenda [0.0]
  This note serves as a cause for thought for scholars interested in researching Decentralized Autonomous Organizations (DAOs) It covers key aspects of data retrieval, data selection criteria, issues in data reliability and validity. The agenda aims to equip scholars with the essential knowledge required to conduct nuanced and rigorous academic studies on DAOs.
  arXiv  Detail & Related papers  (2023-12-28T18:29:40Z)
• Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark [55.898771405172155]
  Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. We provide a systematic overview of the important and recent developments of research on federated learning.
  arXiv  Detail & Related papers  (2023-11-12T06:32:30Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why? [84.46288849132634]
  We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP. We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
  arXiv  Detail & Related papers  (2023-05-22T11:08:00Z)
• A Survey of Deep Visual Cross-Domain Few-Shot Learning [46.08156372869305]
  Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting. We provide a detailed taxonomy of CDFS from the problem setting and corresponding solutions view.
  arXiv  Detail & Related papers  (2023-03-16T12:06:59Z)
• A Comprehensive Survey on Source-free Domain Adaptation [69.17622123344327]
  The research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years. We provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme. We compare the results of more than 30 representative SFDA methods on three popular classification benchmarks.
  arXiv  Detail & Related papers  (2023-02-23T06:32:09Z)
• Mapping Climate Change Research via Open Repositories & AI: advantages and limitations for an evidence-based R&D policy-making [0.0]
  In the last few years, several initiatives have been starting to offer access to research outputs data and metadata in an open fashion. These platforms are opening up scientific production to the wider public and they can be an invaluable asset for evidence-based policy-making. To gain a comprehensive view of entire STI ecosystems, the information provided by each of these resources should be combined and analysed accordingly. Here, we study whether this is the case for the case for mapping Climate Action research in the whole Denmark STI ecosystem, by using 4 popular open access STI data sources.
  arXiv  Detail & Related papers  (2022-09-19T12:56:30Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies [67.34625604583208]
  Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) are considered to engage in Sovereign Data Exchange (SDE) PETs and AETs are technically complex, which impedes their adoption. This study empirically constructs a challenge-oriented technology mapping.
  arXiv  Detail & Related papers  (2022-06-20T08:16:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.