--- title: "SWAN-SF: papers citing this dataset" description: "5 papers in the tracked journals link to the Harvard Dataverse dataset SWAN-SF in their Crossref records, the first in 2022; most of them (2) in 2026." url: https://scholar4ai.com/code/dataverse-ebcfkm markdown_url: https://scholar4ai.com/code/dataverse-ebcfkm.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "5 papers in the tracked journals link to the Harvard Dataverse dataset SWAN-SF in their Crossref records, the first in 2022; most of them (2) in 2026. (as of 2026-11-03)" data_url: https://scholar4ai.com/data/rankings.json updated: 2026-11-03T14:43:26+09:00 window: 2026-10-05 to 2026-11-03 source: OpenAlex (CC0) --- # SWAN-SF: papers linking to it Research papers in the 480 tracked journals that link to SWAN-SF (https://doi.org/10.7910/DVN/EBCFKM), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from. - Harvard Dataverse - Dataset - 2020 - https://doi.org/10.7910/DVN/EBCFKM All repositories: https://scholar4ai.com/code.md ## Key findings - 5 papers in the tracked journals link to the Harvard Dataverse dataset SWAN-SF in their Crossref records, the first in 2022; most of them (2) in 2026. - By category: AI (5); the journals with the most are Neural Networks (2), Expert Systems with Applications (1) and Neural Computing and Applications (1). - It is the own code or data of 1 paper here, the first “Anomaly detection in streaming data: A comparison and evaluation study” (Expert Systems with Applications, 2023). - 4 of the papers build on one another: a later paper cites an earlier one that also links SWAN-SF, or uses the code an earlier paper published. ## By year *Papers linking to SWAN-SF by publication year* | Year | Papers | |---|---:| | 2022 | 1 | | 2023 | 1 | | 2025 | 1 | | 2026 | 2 | ## Journals *Journals whose papers link to SWAN-SF most* | Journal | Papers | |---|---:| | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 2 | | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 | | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 | | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 1 | ## Family tree Each paper sits under the earlier paper it builds on. - [Anomaly detection in streaming data: A comparison and evaluation study](https://doi.org/10.1016/j.eswa.2023.120994) (Expert Systems with Applications, 2023) - [CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series anomaly detection](https://doi.org/10.1016/j.neunet.2025.108129) (Neural Networks, 2025) — uses the code or data the paper above published (without citing it) - [A noise robust and distribution-adaptive framework for multivariate time series anomaly detection](https://doi.org/10.1016/j.neunet.2026.108695) (Neural Networks, 2026) — uses the code or data the paper above published (without citing it) - [Value and shape-aware transformer with prior-enhanced self-attention for multivariate time series classification](https://doi.org/10.1007/s10618-026-01224-9) (Data Mining and Knowledge Discovery, 2026) — uses the code or data the paper above published (without citing it) ## Papers *Papers linking to SWAN-SF, most cited first* | Published | Title | Journal | Authors | Citations | Link | |---|---|---|---|---:|---| | 2023-07-17 | [Anomaly detection in streaming data: A comparison and evaluation study](https://doi.org/10.1016/j.eswa.2023.120994) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Félix Iglesias, Alexander Hartl, Tanja Zseby et al. | 35 | own code or data | | 2022-05-30 | [CGAN-based synthetic multivariate time-series generation: a solution to data scarcity in solar flare forecasting](https://doi.org/10.1007/s00521-022-07361-8) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Yang Chen, Dustin Kempton, Azim Ahmadzadeh et al. | 18 | cites it | | 2025-09-22 | [CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series anomaly detection](https://doi.org/10.1016/j.neunet.2025.108129) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Ke Liu, Mengxuan Li, Jiajun Bu et al. | 4 | cites it | | 2026-02-10 | [A noise robust and distribution-adaptive framework for multivariate time series anomaly detection](https://doi.org/10.1016/j.neunet.2026.108695) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Yanling Du, Ziliang Yang, Baozeng Chang et al. | 1 | cites it | | 2026-06-18 | [Value and shape-aware transformer with prior-enhanced self-attention for multivariate time series classification](https://doi.org/10.1007/s10618-026-01224-9) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Wenjie Xi, Rundong Zuo, Alejandro Alvarez et al. | 0 | cites it | ## How this is measured Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month. ## Cite this page Scholar4AI. (2026). SWAN-SF: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/dataverse-ebcfkm ```bibtex @misc{scholar4ai-code-dataverse-ebcfkm, author = {{Scholar4AI}}, title = {SWAN-SF: papers linking to it}, year = {2026}, howpublished = {\url{https://scholar4ai.com/code/dataverse-ebcfkm}}, urldate = {2026-11-03}, note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0} } ``` License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).