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On the suitability of hugging face hub for empirical studies

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Abstract

Context

Empirical studies in software engineering mainly rely on the data available on code-hosting platforms, being GitHub the most representative. Nevertheless, in the last years, the emergence of Machine Learning (ML) has led to the development of platforms specifically designed for hosting ML-based projects, with Hugging Face Hub (HFH) as the most popular one. So far, there have been no studies evaluating the potential of HFH for such studies.

Objective

We aim at performing an exploratory study of the current state of HFH and its suitability to be used as a source platform for empirical studies.

Method

We conduct a qualitative and quantitative analysis of HFH. The former will be performed by comparing the features of HFH with those of other code-hosting platforms, such as GitHub and GitLab. The latter will be performed by analyzing the data available in HFH.

Results

We propose a feature framework to characterize HFH and report on the current usage of the platform, both in terms of number and types of projects (and surrounding community) and the features they mostly rely on.

Conclusions

The results confirm that HFH offers enough features and diverse enough data to be the source of relevant empirical studies on the development, evolution and usage of AI-related projects. The results also triggered a discussion on aspects of HFH that should be considered when performing such empirical studies.

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Data Availability

The data used in RQ1 (i.e., survey results and interview transcription) is available in a Zenodo repository with the identifier https://doi.org/10.5281/zenodo.11072131. The data used in RQ2 is the October 2023 release of HFCommunity, available with the identifier https://doi.org/10.5281/zenodo.10020642.

Notes

  1. https://huggingface.co/docs/hub/model-card-annotated

  2. https://huggingface.co/docs/api-inference

  3. https://huggingface.co/docs/hub/model-cards#specifying-a-base-model

  4. https://huggingface.co/docs/dataset-viewer/en/parquet

  5. https://huggingface.co/search/full-text

  6. https://huggingface.co/collections

  7. https://huggingface.co/posts

  8. In particular, BitBucket, Codeberg, Forgejo, GitHub, GitLab, HFH, Kallithea, Launchpad, Savannah GNU and SourceForge.

  9. This study was performed on October, 2023.

  10. https://ieeexplore.ieee.org/Xplore/home.jsp

  11. https://dl.acm.org/

  12. https://www.sciencedirect.com/

  13. Queries available in Appendix A.1.1

  14. Queries available in Appendix A.1.2

  15. Query available in Appendix A.1.3

  16. https://www.gharchive.org/

  17. https://huggingface.co/docs/hub/en/repositories-next-steps#how-to-duplicate-or-fork-a-repo-including-lfs-pointers

  18. https://huggingface.co/spaces/huggingface-projects/repo_duplicator

  19. https://git-scm.com/book/en/v2/Git-Internals-Git-References

  20. https://huggingface.co/docs/hub/index

  21. An example of a complete social profile: https://huggingface.co/clefourrier

  22. https://som-research.github.io/HFCommunity/diagram.html

  23. Libraries supported by HFH: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts

  24. https://about.readthedocs.com/?ref=readthedocs.com

  25. For more information: https://huggingface.co/spaces/DIBT/prompt-collective

  26. https://huggingface.co/datasets/DIBT/10k_prompts_ranked

  27. https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard

  28. https://huggingface.co/TheBloke

  29. https://huggingface.co/teknium

  30. https://github.com/huggingface/huggingface_hub

References

  • Ait A, Izquierdo JLC, Cabot J (2022) An empirical study on the survival rate of github projects. In: Int. Conf. on Mining Software Repositories, pp 365–375

  • Ait A, Cánovas Izquierdo JL, Cabot J (2023) HFCommunity: a Tool to Analyze the Hugging Face Hub Community. Int. Conf. on Software Analysis, Evolution and Reengineering, pp 728–732

    Google Scholar 

  • Ait A, Izquierdo JLC, Cabot J (2023b) On the suitability of hugging face hub for empirical studies. arXiv:2307.14841

  • Akhtar M, Benjelloun O, Conforti C, Gijsbers P, Giner-Miguelez J, Jain N, Kuchnik M, Lhoest Q, Marcenac P, Maskey M, Mattson P, Oala L, Ruyssen P, Shinde R, Simperl E, Thomas G, Tykhonov S, Vanschoren J, van der Velde J, Vogler S, Wu C (2024) Croissant: A metadata format for ml-ready datasets. In: Workshop on Data Management for End-to-End Machine Learning, pp 1–6

  • Al-Rubaye A, Sukthankar G (2023) Improving Code Review with GitHub Issue Tracking. In: Int. Conf. on advances in social networks analysis and mining, p 210-217

  • Alamer G, Alyahya S (2017) Open Source Software Hosting Platforms: A Collaborative Perspective’s Review. J Softw 12(4):274–291

    Article  Google Scholar 

  • Baltes S, Kiefer R, Diehl S (2017) Attribution Required: Stack Overflow Code Snippets in GitHub Projects. In: Int. conf. on software engineering Companion, pp 161–163

  • Baltes S, Knack J, Anastasiou D, Tymann R, Diehl S (2018) (No) Influence of Continuous Integration on the Commit Activity in GitHub Projects. In: ACM SIGSOFT Int. Workshop on Software Analytics, pp 1–7

  • Bao L, Xia X, Lo D, Murphy GC (2021) A Large Scale Study of Long-Time Contributor Prediction for GitHub Projects. IEEE Trans Software Eng 47(6):1277–1298

    Article  Google Scholar 

  • Bäumer FS, Dollmann M, Geierhos M (2017) Studying Software Descriptions in SourceForge and App Stores for a Better Understanding of Real-Life Requirements. In: ACM SIGSOFT Int. Workshop on App Market Analytics, pp 19–25

  • Biazzini M, Baudry B (2014) “May the Fork Be with You”: Novel Metrics to Analyze Collaboration on GitHub. In: Int. Workshop on Emerging Trends in Software Metrics, pp 37–43

  • Borges H, Tulio Valente M (2018) What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform. J Syst Softw 146:112–129

    Article  Google Scholar 

  • Cai X, Zhu J, Shen B, Chen Y (2016) GRETA: Graph-Based Tag Assignment for GitHub Repositories. Annual computer software and applications conference 1:63–72

    Google Scholar 

  • Casalnuovo C, Suchak Y, Ray B, Rubio-González C (2017) GitcProc: a tool for processing and classifying GitHub commits. In: ACM SIGSOFT Int. symposium on software testing and analysis, pp 396–399

  • Castaño J, Martínez-Fernández S, Franch X, Bogner J (2023a) Analyzing the evolution and maintenance of ML models on hugging face. arXiv:2311.13380

  • Castaño J, Martínez-Fernández S, Franch X, Bogner J (2023b) Exploring the carbon footprint of hugging face’s ML models: A repository mining study. In: Int. symposium on empirical software engineering and measurement, pp 1–12

  • Chen D, Stolee KT, Menzies T (2019) Replication Can Improve Prior Results: A GitHub Study of Pull Request Acceptance. In: Int. Conf. on Program Comprehension, pp 179–190

  • Cosentino V, Cánovas Izquierdo JL, Cabot J (2016) Findings from GitHub: Methods, Datasets and Limitations. In: Int. conf. on mining software repositories, pp 137–141

  • Cosentino V, Cánovas Izquierdo JL, Cabot J (2017) A Systematic Mapping Study of Software Development with GitHub. IEEE Access 5:7173–7192

    Article  Google Scholar 

  • Croft R, Xie Y, Zahedi M, Babar MA, Treude C (2022) An empirical study of developers’ discussions about security challenges of different programming languages. Empir Softw Eng 27(1):27

    Article  Google Scholar 

  • Dabbish LA, Stuart HC, Tsay J, Herbsleb JD (2012) Social coding in github: transparency and collaboration in an open software repository. In: Conf. on computer supported cooperative work, pp 1277–1286

  • Dabic O, Aghajani E, Bavota G (2021) Sampling projects in github for MSR studies. Int. Conf. on mining software repositories, IEEE, pp 560–564

    Google Scholar 

  • Decan A, Mens T, Claes M, Grosjean P (2016) When GitHub Meets CRAN: An Analysis of Inter-Repository Package Dependency Problems. In: Int. Conf. on Software Analysis, Evolution, and Reengineering, pp 493–504

  • Demeyer S, Murgia A, Wyckmans K, Lamkanfi A (2013) Happy Birthday! a Trend Analysis on Past Msr Papers. In: Int. working conf. on mining software repositories, pp 353–362

  • Destefanis G, Ortu M, Bowes D, Marchesi M, Tonelli R (2018) On Measuring Affects of Github Issues’ Commenters. In: Int. workshop on emotion awareness in software engineering, pp 14–19

  • Dyer R, Nguyen HA, Rajan H, Nguyen TN (2015) Boa: Ultra-Large-Scale Software Repository and Source-Code Mining. ACM Trans Softw Eng Methodol 25(1)

  • Eibl G, Thurnay L (2023) The Promises and Perils of Open Source Software Release and Usage by Government - Evidence from GitHub and Literature. In: Int. conf. on digital government research, pp 180–190

  • English R, Schweik CM (2007) Identifying Success and Tragedy of FLOSS Commons: A Preliminary Classification of Sourceforge.net Projects. In: Int. Workshop on emerging trends in floss research and development, pp 11–11

  • Eraslan S, Kopec-Harding K, Jay C, Embury SM, Haines R, Cortés Ríos JC, Crowther P (2020) Integrating GitLab metrics into coursework consultation sessions in a software engineering course. J Syst Softw 167:110613

    Article  Google Scholar 

  • Fairbanks J, Tharigonda A, Eisty NU (2023) Analyzing the Effects of CI/CD on Open Source Repositories in GitHub and GitLab. Int. Conf. on Software Engineering Research, Management and Applications, pp 176–181

    Google Scholar 

  • Flint SW, Chauhan J, Dyer R (2022) Pitfalls and Guidelines for Using Time-based Git Data. Empir Softw Eng 27(7):194

    Article  Google Scholar 

  • Foushee B, Krein JL, Wu J, Buck R, Knutson CD, Pratt LJ, MacLean AC (2013) Reflexivity, Raymond, and the Success of Open Source Software Development: A SourceForge Empirical Study. In: Int. conf. on evaluation and assessment in software engineering, pp 246–251

  • Gajanayake R, Hiras M, Gunathunga P, Janith Supun EG, Karunasenna A, Bandara P (2020) Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer. In: Int. conf. on advancements in computing, pp 168–173

  • Giner-Miguelez J, Gómez A, Cabot J (2024) Describeml: A dataset description tool for machine learning. Sci Comput Program 231:103030

    Article  Google Scholar 

  • Golzadeh M, Decan A, Legay D, Mens T (2021) A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments. J Syst Softw 175:110911

    Article  Google Scholar 

  • Gonzalez D, Zimmermann T, Nagappan N (2020) The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub. In: Int. conf. on mining software repositories, pp 431–442

  • Gousios G, Spinellis D (2012) GHTorrent: Github’s data from a firehose. In: Working conf. of mining software repositories, pp 12–21

  • Gousios G, Pinzger M, van Deursen A (2014) An exploratory study of the pull-based software development model. In: Int. conf. on software engineering, pp 345–355

  • Gwebu KL, Wang J (2011) Adoption of Open Source Software: The role of social identification. Decis Support Syst 51:220–229

    Article  Google Scholar 

  • Hauff C, Gousios G (2015) Matching GitHub developer profiles to job advertisements. In: Working conf. on mining software repositories, p 362-366

  • He R, He H, Zhang Y, Zhou M (2023) Automating Dependency Updates in Practice: An Exploratory Study on GitHub Dependabot. IEEE Trans Softw Eng 49(8):4004–4022

    Article  Google Scholar 

  • Hove SE, Anda B (2005) Experiences from Conducting Semi-structured Interviews in Empirical Software Engineering Research. In: Int. Symposium on Software Metrics, p 23

  • Howison J, Crowston K (2004) The Perils and Pitfalls of Mining Sourceforge. In: Int. Workshop on Mining Software Repositories, pp 7–11

  • Imtiaz N, Middleton J, Chakraborty J, Robson N, Bai GR, Murphy-Hill ER (2019) Investigating the effects of gender bias on GitHub. In: Int. Conf. on Software Engineering, pp 700–711

  • Izquierdo JLC, Cabot J (2022) On the analysis of non-coding roles in open source development. Empir Softw Eng 27(1):18

    Article  Google Scholar 

  • Jiang W, Cheung C, Thiruvathukal GK, Davis JC (2023a) Exploring Naming Conventions (and Defects) of Pre-trained Deep Learning Models in Hugging Face and Other Model Hubs. arXiv:2310.01642

  • Jiang W, Synovic N, Hyatt M, Schorlemmer TR, Sethi R, Lu YH, Thiruvathukal GK, Davis JC (2023b) An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry. In: Int. conf. on software engineering, pp 2463–2475

  • Joshi A, Kale S, Chandel S, Pal DK (2015) Likert scale: Explored and explained. British J Appl Sci Technol 7(4):396–403

    Article  Google Scholar 

  • Joshi SD, Chimalakonda S (2019) RapidRelease: A Dataset of Projects and Issues on Github with Rapid Releases. In: Int. conf. on mining software repositories, p 587-591

  • Kaide K, Tamada H (2022) Argo: Projects’ Time-Series Data Fetching and Visualizing Tool for GitHub. In: Int. summer virtual conf. on software engineering, artificial intelligence, networking and parallel/distributed computing, pp 141–147

  • Kalliamvakou E, Gousios G, Blincoe K, Singer L, Germán DM, Damian DE (2014) The Promises and Perils of Mining GitHub. In: Int. working conf. on mining software repositories, pp 92–101

  • Kalliamvakou E, Gousios G, Blincoe K, Singer L, Germán DM, Damian DE (2016) An In-depth Study of the Promises and Perils of Mining GitHub. Empir Softw Eng 21(5):2035–2071

    Article  Google Scholar 

  • Kathikar A, Nair A, Lazarine B, Sachdeva A, Samtani S (2023) Assessing the vulnerabilities of the open-source artificial intelligence (AI) landscape: A large-scale analysis of the hugging face platform. In: Int. conf. on intelligence and security informatics, pp 1–6

  • Kleinbaum DG, Klein M (2005) Survival Analysis: A Self-Learning Text. Springer Science and Business Media, LLC

  • Kritikos A, Chatziasimidis F (2011) SFparser: A Tool for Selectively Parsing SourceForge. In: Panhellenic conf. on informatics, pp 161–165

  • Lazarine B, Zhang Z, Sachdeva A, Samtani S, Zhu H (2022) Exploring the Propagation of Vulnerabilities from GitHub Repositories Hosted by Major Technology Organizations. In: Workshop on cyber security experimentation and test, pp 145–150

  • Liao Z, Yi M, Wang Y, Liu S, Liu H, Zhang Y, Zhou Y (2019) Healthy or not: A way to predict ecosystem health in github. Symmetry 11(2):144

    Article  Google Scholar 

  • Malan DJ (2022) Standardizing Students’ Programming Environments with Docker Containers: Using Visual Studio Code in the Cloud with GitHub Codespaces. In: ACM Conf. on innovation and technology in computer science education, pp 599–600

  • Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, Spitzer E, Raji ID, Gebru T (2019) Model cards for model reporting. In: Conf. on fairness, accountability, and transparency, pp 220–229

  • Montandon JE, Valente MT, Silva LL (2021) Mining the Technical Roles of GitHub Users. Inf Softw Technol 131:106485

    Article  Google Scholar 

  • Mu W, Bian Y, Zhao JL (2019) The role of online leadership in open collaborative innovation. Ind Manag Data Syst 119(9):1969–1987

    Article  Google Scholar 

  • Özçevik Y, Altay O (2023) MetricHunter: A software metric dataset generator utilizing SourceMonitor upon public GitHub repositories. SoftwareX 23:101499

    Article  Google Scholar 

  • Pina D, Goldman A, Seaman C (2022) Sonarlizer xplorer: a tool to mine github projects and identify technical debt items using SonarQube. In: Int. Conf. on Technical Debt, p 71-75

  • Qiu HS, Nolte A, Brown A, Serebrenik A, Vasilescu B (2019) Going farther together: the impact of social capital on sustained participation in open source. In: Int. conf. on software engineering, pp 688–699

  • Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. In: Conf. on empirical methods in natural language processing, pp 3980–3990

  • Ren L, Zhou S, Kästner C (2018) Forks Insight: Providing an Overview of GitHub Forks. In: Int. conf. on software engineering: companion proceeedings, pp 179–180

  • Rigney D (2010) The Matthew effect: How advantage begets further advantage. Columbia University Press

  • Robles G (2010) Replicating MSR: a Study of the Potential Replicability of Papers Published in the Mining Software Repositories Proceedings. In: Int. working conf. on mining software repositories, pp 171–180

  • Robles G, Ho-Quang T, Hebig R, Chaudron MRV, Fernandez MA (2017) An Extensive Dataset of UML Models in GitHub. In: Int. conf. on mining software repositories, pp 519–522

  • Romano S, Caulo M, Buompastore M, Guerra L, Mounsif A, Telesca M, Baldassarre MT, Scanniello G (2021) G-Repo: a Tool to Support MSR Studies on GitHub. In: Int. Conf. on software analysis, evolution and reengineering, pp 551–555

  • Safari H, Sabri N, Shahsavan F, Bahrak B (2020) An Analysis of GitLab’s Users and Projects Networks. In: Int. Symposium onTelecommunications, pp 194–200

  • Sanh V, Wolf T, Ruder S (2019) A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks. In: Conf. on artificial intelligence, pp 6949–6956

  • Souza I, Campello L, Rodrigues E, Guedes G, Bernardino M (2021) An Analysis of Automated Code Inspection Tools for Php Available on GitHub Marketplace. In: Symp. on systematic and automated software, pp 10–17

  • Spinellis D, Kotti Z, Mockus A (2020) A Dataset for GitHub Repository Deduplication. In: Int. conf. on mining software repositories, pp 523–527

  • Squire M (2017) The Lives and Deaths of Open Source Code Forges. In: Int. symposium on open collaboration, opensym, pp 15:1–15:8

  • Tsay J, Dabbish L, Herbsleb J (2014) Let’s Talk about It: Evaluating Contributions through Discussion in GitHub. In: ACM SIGSOFT Int. symposium on foundations of software engineering, pp 144–154

  • Valenzuela-Toledo P, Bergel A, Kehrer T, Nierstrasz O (2023) EGAD: A moldable tool for GitHub Action analysis. In: Int. conf. on mining software repositories, pp 260–264

  • Wachs J, Nitecki M, Schueller W, Polleres A (2022) The Geography of Open Source Software: Evidence from GitHub. Technological Forecasting Social Change 176:121478

    Article  Google Scholar 

  • Wang J, Zhang X, Chen L, Xie X (2022) Personalizing label prediction for GitHub issues. Inf Soft Technol 145:106845

    Article  Google Scholar 

  • Wessel M, Serebrenik A, Wiese I, Steinmacher I, Gerosa MA (2020) What to Expect from Code Review Bots on GitHub? A Survey with OSS Maintainers. In: Brazilian symposium on software engineering, pp 457–462

  • Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B (2012) Experimentation in Software Engineering. Springer

    Book  Google Scholar 

  • Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, von Platen P, Ma C, Jernite Y, Plu J, Xu C, Scao TL, Gugger S, Drame M, Lhoest Q, Rush AM (2020) Transformers: State-of-the-Art Natural Language Processing. In: Conf. on empirical methods in natural language processing, pp 38–45

  • Wolter T, Barcomb A, Riehle D, Harutyunyan N (2023) Open Source License Inconsistencies on GitHub. ACM Trans Softw Eng Methodol 32(5)

  • Wu J, He H, Xiao W, Gao K, Zhou M (2022) Demystifying Software Release Note Issues on GitHub. In: Int. conf. on program comprehension, pp 602–613

  • Yang X, Liang W, Zou J (2024) Navigating dataset documentations in AI: A large-scale analysis of dataset cards on hugging face. arXiv:2401.13822

  • You K, Liu Y, Zhang Z, Wang J, Jordan MI, Long M (2022) Ranking and tuning pre-trained models: A new paradigm for exploiting model hubs. J Mach Learn Res 23:209:1–209:47

  • Yu Y, Yin G, Wang H, Wang T (2014) Exploring the patterns of social behavior in GitHub. In: Int. workshop on crowd-based software development methods and technologies, pp 31–36

  • Yu Y, Wang H, Filkov V, Devanbu P, Vasilescu B (2015) Wait for It: Determinants of Pull Request Evaluation Latency on GitHub. In: Working conf. on mining software repositories, pp 367–371

  • Yu Y, Li Z, Yin G, Wang T, Wang H (2018) A Dataset of Duplicate Pull-Requests in Github. In: Int. Conf. on Mining Software Repositories, p 22-25

  • Zou W, Zhang W, Xia X, Holmes R, Chen Z (2019) Branch Use in Practice: A Large-Scale Empirical Study of 2,923 Projects on GitHub. In: Int. conf. on software quality, reliability and security, pp 306–317

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Acknowledgements

This work is part of the project TED2021-130331B-I00 funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR; and BESSER, funded by the Luxembourg National Research Fund (FNR) PEARL program, grant agreement 16544475.

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A. Appendix

A. Appendix

1.1 A.1 Queries of Digital Libraries

1.1.1 A.1.1 Review of platform studies

To check how many articles are published for each platform, we queried each digital library with each one of the ten platforms identified in Section 4.2.1.\(^a\)

\(^a\) Platforms with names with a possible space separator have been searched with both forms (e.g., GitHub or Git Hub, HuggingFace or Hugging Face, etc.)

figure i
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1.1.2 A.1.2 Review of Literature

In order to show a generalization of the queries, we show the structure we follow, in which the platform keyword is one of four code-hosting platforms identified with results in Section 5.1 (Review of literature step), and the feature keyword can be one of the following values: Branches, CICD - Development Workflow, Collaboration/Cloud Coding,\(^b\) Code Review, CVS, External Integrations, Following, Fork, Groups, Issues, Licensing, Marketplace, Packages, Pull Request, Project Relations, Q&A, Release, Repo Type, Roles, Security, Snippets, Stream Analytics, Tagging, Webhooks, Wiki, Work Management,\(^c\) Web Publish.\(^d\)

\(^b\) In GitHub we used the keyword Codespaces.

\(^c\) In GitHub we used the keyword GitHub Projects.

\(^d\) In GitHub and GitLab we used the keyword Pages.

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1.1.3 A.1.3 Review of Datasets

The platform keyword is one of the four code-hosting platforms identified with results in Section 5.1 (Review of literature step) and the data keyword is either: data source, dataset or tool.

figure o
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1.2 B.2 Top-100 Repositories

Table 10 Top-100 downloaded repositories of HFH (October 2023)
Table 11 Top-100 liked repositories of HFH (October 2023)

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Ait, A., Cánovas Izquierdo, J.L. & Cabot, J. On the suitability of hugging face hub for empirical studies. Empir Software Eng 30, 57 (2025). https://doi.org/10.1007/s10664-024-10608-8

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