Signal Processing Information Base (SPIB) — curated mirror & documentation
Bibliographic record
Abstract
The Signal Processing Information Base (SPIB) was originally created by Don H. Johnson at Rice University in the early 1990s, with sponsorship from the IEEE Signal Processing Society (SPS) and NSF. It was described in Johnson & Shami (1993, IEEE Signal Processing Magazine, doi:10.1109/79.248556) and served for decades as a reference repository for signal processing datasets. After the Rice University site became unavailable, Prof. Johnson kindly provided a complete backup in 2013, enabling restoration. Since then, SPIB has been hosted by the Circuits and Signal Processing Laboratory (LINSE) at the Federal University of Santa Catarina, and curated by Prof. Eduardo V. Kuhn (currently, at UTFPR). This Zenodo record provides:- Documentation (README.md, HISTORY.md, LICENSE.txt)- Citation guidance (CITATION.cff)- Historical context and manifest templates- Persistent DOI to enable future citation and referencing Canonical site (data access): http://spib.linse.ufsc.br **SPIB – History (concise)** - 1993 — SPIB described by Johnson & Shami, SPS/NSF sponsorship. - ~1993–2013 — Hosted at Rice University under Prof. Don H. Johnson. - 2013-07-21 — Full site backup recovered; restoration initiated at LINSE/UFSC. - 2013–present — Canonical hosting at LINSE/UFSC; curation by E. V. Kuhn (currently UTFPR). - 2025-09-03 — Latest metadata refresh prior to Zenodo record creation. *Please cite both the original SPIB article (Johnson & Shami, 1993) and this curated record.* 🔹 APA (7th ed) Johnson, D. H., & Shami, P. N. (1993). The Signal Processing Information Base. IEEE Signal Processing Magazine, 10(4), 36–42. https://doi.org/10.1109/79.248556 Signal Processing Information Base (SPIB) — curated mirror & documentation. (2025, September 4). Zenodo. https://doi.org/10.5281/zenodo.17054746 🔹 ABNT (NBR 6023:2018) JOHNSON, Don H.; SHAMI, Patrick Nabeel. The Signal Processing Information Base. IEEE Signal Processing Magazine, v. 10, n. 4, p. 36–42, 1993. DOI: https://doi.org/10.1109/79.248556. Signal Processing Information Base (SPIB) — curated mirror & documentation. Zenodo, 2025. DOI: https://doi.org/10.5281/zenodo.17054746. 🔹 Vancouver Johnson DH, Shami PN. The Signal Processing Information Base. IEEE Signal Processing Magazine. 1993;10(4):36–42. doi:10.1109/79.248556. Signal Processing Information Base (SPIB) — curated mirror & documentation [dataset on the Internet]. Zenodo; 2025. Available from: https://doi.org/10.5281/zenodo.17054746
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.554 | 0.673 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".