Assessing Scientific Contributions in Data Sharing Spaces

Abstract

In the present academic landscape, the process of collecting data is slow, and the lax infrastructures for data collaborations lead to significant delays in coming up with and disseminating conclusive findings. Therefore, there is an increasing need for a secure, scalable, and trustworthy data-sharing ecosystem that promotes and rewards collaborative data-sharing efforts among researchers, and a robust incentive mechanism is required to achieve this objective. Reputation-based incentives, such as the h-index, have historically played a pivotal role in the academic community. However, the h-index suffers from several limitations. This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions. Utilizing the Microsoft Academic Graph and machine learning techniques, the SCIENCEindex predicts the progress made by a researcher over their career and provides a soft incentive for sharing their datasets with peer researchers. To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter. DataCite, a database of openly available datasets, proxies this parameter, which is further enhanced by including a researcher's data-sharing activity. Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index. We observe that it results in a much more even spread of evaluations. The SCIENCE-index is a crucial component in constructing a decentralized protocol that promotes trust-based data sharing, addressing the current inequity in dataset sharing. The work outlined in this paper provides the foundation for assessing scientific contributions in future data-sharing spaces powered by decentralized applications.

Semantics-based Framework for Incentivized Research Data Sharing

“…The idea of such an index is to provide a standard for researchers to compare each other when seeking or sharing data. The index is detailed in our previous work (Adams et al 2023). This paper details the use of a linear model for ranking researchers in comparison to their peers.…”

Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits

“…Adams et al [23] have recently taken a commendable step toward fostering a culture of data sharing and collaboration in scientific research by refining their original SCIENCE-index model. The improved version, named the SCIENCE-index Augmented, leverages the robustness of blockchain technology to provide a more comprehensive evaluation of a researcher's scientific contributions, including data sharing [23]. The SCIENCE-index Augmented is a comprehensive system that evaluates researchers' contributions using five key parameters.…”

“…The updated model includes a measure of a researcher's data sharing frequency, thus serving as tangible proof of their active involvement in collaborative research. The SCIENCE-index Augmented increased progressively as a researcher increasingly partakes in data sharing, owing to its specifically designed computation formula [23]. Table 3 shows the primary advantages and disadvantages of the SCIENCE-index Augmented.…”

“…Significantly, these metrics are not readily available on widely used platforms such as Scopus, Google Scholar, or ResearchGate, where researchers typically monitor their h-index. In 2023, a new author-specific metric, the SCIENCE-index Augmented, was proposed by Adams et al [23]. This metric holds considerable promise for forecasting a researcher's future trajectory based on their prior scientific contributions.…”

“…This metric holds considerable promise for forecasting a researcher's future trajectory based on their prior scientific contributions. Nevertheless, the computational demands of the blockchain-based model need to be minimized to prevent excessive transaction costs on public virtual machines [23]. There is also a concern that this model could facilitate simpler data publishing than paper publications, potentially causing artificial inflation of a researcher's SCIENCE-index Augmented score when less valuable datasets are shared [23].…”