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Leveraging AI and Data Visualization for Enhanced Policy-Making: Aligning Research Initiatives with Sustainable Development Goals

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Listed:
  • Maicon Herverton Lino Ferreira da Silva Barros

    (Programa de Pós-Graduação em Engenharia de Computação (PPGEC), Universidade de Pernambuco (UPE), Recife 50050-000, Brazil
    These authors contributed equally to this work.)

  • Leonides Medeiros Neto

    (Programa de Pós-Graduação em Engenharia de Computação (PPGEC), Universidade de Pernambuco (UPE), Recife 50050-000, Brazil
    These authors contributed equally to this work.)

  • Guto Leoni Santos

    (Business School, Dublin City University (DCU), D09 RFK0 Dublin, Ireland
    These authors contributed equally to this work.)

  • Roberto Cesar da Silva Leal

    (Sistemas de Informação, Universidade de Pernambuco (UPE), Caruaru 55002-917, Brazil
    These authors contributed equally to this work.)

  • Raysa Carla Leal da Silva

    (Sistemas de Informação, Universidade de Pernambuco (UPE), Caruaru 55002-917, Brazil
    These authors contributed equally to this work.)

  • Theo Lynn

    (Business School, Dublin City University (DCU), D09 RFK0 Dublin, Ireland
    These authors contributed equally to this work.)

  • Raphael Augusto Dourado

    (Programa de Pós-Graduação em Engenharia de Computação (PPGEC), Universidade de Pernambuco (UPE), Recife 50050-000, Brazil
    These authors contributed equally to this work.)

  • Patricia Takako Endo

    (Programa de Pós-Graduação em Engenharia de Computação (PPGEC), Universidade de Pernambuco (UPE), Recife 50050-000, Brazil
    These authors contributed equally to this work.)

Abstract

Scientists, research institutions, funding agencies, and policy-makers have all emphasized the need to monitor and prioritize research investments and outputs to support the achievement of the United Nations Sustainable Development Goals (SDGs). Unfortunately, many current and historic research publications, proposals, and grants were not categorized against the SDGs at the time of submission. Manual post hoc classification is time-consuming and prone to human biases. Even when classified, few tools are available to decision makers for supporting resource allocation. This paper aims to develop a deep learning classifier for categorizing research abstracts by the SDGs and a decision support system for research funding policy-makers. First, we fine-tune a Bidirectional Encoder Representations from Transformers (BERT) model using a dataset of 15,488 research abstracts from authors at leading Brazilian universities, which were preprocessed and balanced for training and testing. Second, we present a PowerBI dashboard that visualizes classifications for supporting informed resource allocation for sustainability-focused research. The model achieved an F1-score, precision, and recall exceeding 70% for certain classes and successfully classified existing projects, thereby enabling better tracking of Agenda 2030 progress. Although the model is capable of classifying any text, it is specifically optimized for Brazilian research due to the nature of its fine-tuning data.

Suggested Citation

  • Maicon Herverton Lino Ferreira da Silva Barros & Leonides Medeiros Neto & Guto Leoni Santos & Roberto Cesar da Silva Leal & Raysa Carla Leal da Silva & Theo Lynn & Raphael Augusto Dourado & Patricia T, 2024. "Leveraging AI and Data Visualization for Enhanced Policy-Making: Aligning Research Initiatives with Sustainable Development Goals," Sustainability, MDPI, vol. 16(24), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11050-:d:1545415
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    References listed on IDEAS

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    1. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Casey Stevens & Norichika Kanie, 2016. "The transformative potential of the Sustainable Development Goals (SDGs)," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 16(3), pages 393-396, June.
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