Applications of Open Data in Different Disciplines

Topic Information

Dear Colleagues,

Open data refers to datasets that are freely accessible, reusable, and shareable without restrictions, serving as a foundation for transparency, reproducibility, and innovation in science and policy. Its potential lies in fostering collaboration, enabling evidence-based decision-making, and driving technological and societal advances, among others. At the same time, challenges remain, including privacy risks, uneven data quality, lack of contextualization, and unequal access to infrastructure and expertise. 

We are pleased to invite you to submit papers to this Topic, which aims to gather contributions that explore both the opportunities and limitations of open data, highlighting its role in strengthening scientific rigor and expanding its applications across disciplines, while connecting diverse aspects of academic culture in the advent of open science. Beyond information availability, this focused edition emphasizes the critical use of open data in research practices, tackling questions of procedural reproducibility, methodological transparency, epistemic assumptions, and the ways in which they reshape academic collaboration, outcome evaluation, and knowledge circulation.

In this Topic, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following contexts:

1. Urban–Territorial

• Smart urban planning and adaptive land use:

Providing frameworks for integrating geospatial open data into investigations at different scales. The scope encompasses model testing, cross‑city comparisons, and the advancement of methodological rigor in the analysis of landscapes and territories.

• Active mobility and urban systems:

Offering empirical datasets for displacements and transportation research, enabling reproducible interpretations of accessibility, congestion, and modal shifts, as well as information on other infrastructures and public services. The remit includes comparative assessments and supports evidence-based policy recommendations.

• Circular economy and resource management:

Ensuring transparency in asset flows to quantify viability metrics and contrast economic practices. The extent involves academic debates on urban metabolism and long-term development, grounding them in open and traceable data.

2. The Technical-Technological

This scope may also be explored, considering:

• Data interoperability and innovative standards:

Presenting methodological debates in information science by emphasizing the need for standardized protocols. The framework covers the reproducibility of research procedures and promotes cross-disciplinary data integration in publications.

• Artificial intelligence and predictive analytics:

Allocating fertile ground for computer science and applied studies, where open datasets are essential for training and validating models. The range ensures transparency in AI investigation and supports critical academic debates on bias and ethics.

• Cybersecurity and data privacy:

Bolstering academic discourse on digital ethics, law, and information security, with open data initiatives raising critical questions about balancing openness and protection. The dimension aims to enrich scholarly debates on governance of digital infrastructures.

We look forward to receiving your contributions.

Dr. Fábio Duarte
Prof. Dr. Letícia Peret Antunes Hardt
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Data data	2.4	5.4	2016	19.2 Days	CHF 1600	Submit
Encyclopedia encyclopedia	-	10.1	2021	25.3 Days	CHF 1200	Submit
Publications publications	3.4	5.7	2013	26.5 Days	CHF 1600	Submit
Systems systems	3.8	5.4	2013	19.8 Days	CHF 2400	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Data Encyclopedia Publications Systems

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

27 pages, 2972 KB  

Open AccessArticle

An Open-Data-Driven Enhanced Bayesian Decision Network for System-Level UAV Accident-Severity Analysis and Response Simulation

by Ruimin Hao, Anning Ni, Jingbo Yin, Linjie Gao, Yutong Zhu, Xi Wang, Yizhou Wang and Xiaoning Zhang

Systems 2026, 14(8), 1009; https://doi.org/10.3390/systems14081009 - 17 Aug 2026

Viewed by 374

Abstract

Unmanned aerial vehicle (UAV) accidents pose growing challenges to public safety and airspace management. This study develops an open-data-driven hierarchical Bayesian network (BN) with a Leaky Noisy-OR mechanism to analyse factors associated with consequence severity among recorded UAV accidents, and extends it to [...] Read more.

Unmanned aerial vehicle (UAV) accidents pose growing challenges to public safety and airspace management. This study develops an open-data-driven hierarchical Bayesian network (BN) with a Leaky Noisy-OR mechanism to analyse factors associated with consequence severity among recorded UAV accidents, and extends it to a Bayesian decision network for response simulation. Using 633 public accident records and matched meteorological data, 14 binary risk-factor nodes spanning human, machine, environmental, and management dimensions were constructed. Stratified five-fold cross-validation yielded a mean validation F1 score of 0.922 and an AUC of 0.784. Backward inference ranked airspace exposure, wind, and operation error highest under severe-consequence conditioning, whereas sensitivity analysis identified wind, bad weather history, and operation error as the most influential root-node parameters. Under the assumed directed acyclic graph (DAG), the bad weather history→weather→environment→risk state path had the highest average edge-influence score (0.853). Under the baseline safety-priority assumptions, the reroute strategy was preferred, yielding the highest expected utility (31.967) and reducing the model-estimated post-decision high-risk probability from 81% to 45%. Alternative preference settings ranked the adjust strategy first. The framework integrates open-data severity analysis with assumption-explicit response simulation. Full article

(This article belongs to the Topic Applications of Open Data in Different Disciplines)

►▼ Show Figures

Graphical abstract

30 pages, 698 KB  

Open AccessArticle

Chinese Bureaucratic System Algorithm: A Rank-Weighted Institutional Search Framework for Continuous and Routing Decision Systems

by Yuanbo Li and Peixuan Li

Systems 2026, 14(8), 902; https://doi.org/10.3390/systems14080902 - 1 Aug 2026

Viewed by 230

Abstract

Many decision systems require search over continuous parameters or discrete route structures. This study examines whether a rank-weighted population architecture can support both representations while retaining an interpretable decision mechanism. We propose the Chinese Bureaucratic System Algorithm (CBSA), which transforms objective-based population ranks [...] Read more.

Many decision systems require search over continuous parameters or discrete route structures. This study examines whether a rank-weighted population architecture can support both representations while retaining an interpretable decision mechanism. We propose the Chinese Bureaucratic System Algorithm (CBSA), which transforms objective-based population ranks into either continuous movement or edge-based routing probabilities. We expect rank aggregation to be most useful when higher-ranked solutions contain stable information that can be reused in later searches. The framework is instantiated as a continuous variant (CBSA-C) and a discrete routing variant (CBSA-D), and its computational complexity and limiting search properties are analyzed. On the 2014 Congress on Evolutionary Computation (CEC2014) benchmark suite, CBSA-C obtains the best average rank among five algorithms and is strongest on multimodal and hybrid functions. On 50 capacitated vehicle routing instances, CBSA-D ranks second behind iterated local search while outperforming four population- or edge-based baselines. Across all 56 Solomon time-window routing instances, it is most competitive on clustered classes and weaker on random and mixed classes. These results support a conditional interpretation: rank-weighted aggregation is useful when objective ranks are informative and the representation contains reusable structure, while stronger local-search methods remain preferable when such structure is absent. Full article

(This article belongs to the Topic Applications of Open Data in Different Disciplines)

►▼ Show Figures

Figure 1

18 pages, 443 KB  

Open AccessArticle

Evaluating Automated Event Databases for Event Forecasting: A Comparative Analysis of GDELT and POLECAT

by Kelang Zhao, Zexin Fu, Yan Pan and Xin Zhang

Data 2026, 11(7), 158; https://doi.org/10.3390/data11070158 - 30 Jun 2026

Viewed by 1621

Abstract

To address the lack of systematic quantitative evaluation of automated event repositories in forecasting tasks, this study selected Global Database of Events, Language, and Tone (GDELT) and the emerging Political Event Classification, Attributes, and Types (POLECAT) dataset as research subjects, aiming to provide [...] Read more.

To address the lack of systematic quantitative evaluation of automated event repositories in forecasting tasks, this study selected Global Database of Events, Language, and Tone (GDELT) and the emerging Political Event Classification, Attributes, and Types (POLECAT) dataset as research subjects, aiming to provide a basis for data source selection through multidimensional comparisons. The primary research question is how to establish a structured, quantitative framework to reliably evaluate these data sources in specific predictive contexts. This study constructed a quantitative framework covering scale, coverage, redundancy, and accuracy, and conducted empirical forecasting tests across multiple cities. The results indicate that while GDELT possesses a large-scale and high media coverage, it performs poorly in terms of redundancy and domain accuracy; although POLECAT is smaller in scale, it exhibits high domain identification accuracy and extremely low redundancy, with its forecast results demonstrating superior precision and false positive control capabilities. The conclusion is that GDELT is suitable for macro-level early warning scenarios requiring high recall, while POLECAT is better suited for tasks requiring high signal-to-noise ratio inputs and specific regional studies; the choice between the two should be based on a trade-off between model requirements and application scenarios. Full article

(This article belongs to the Topic Applications of Open Data in Different Disciplines)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-3