Evidence map›Paper›PMID 41053171›Full record

ArticleScientific data2025

Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments.

Davide Privitera, Alessandro Mecocci, Sandro Bartolini

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Davide PriviteraDepartment of Information Engineering and Mathematics, University of Siena, Via Roma 56, Siena, 53100, Italy. privitera@diism.unisi.it.ORCID 0000-0001-5711-9107
Alessandro MecocciDepartment of Information Engineering and Mathematics, University of Siena, Via Roma 56, Siena, 53100, Italy.
Sandro BartoliniDepartment of Information Engineering and Mathematics, University of Siena, Via Roma 56, Siena, 53100, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We publish a comprehensive dataset of peristaltic pump dosing outputs in pharmaceutical manufacturing, where accuracy is crucial for drug quality and patient safety, consisting of 149,847 measurements spanning volumes from 0.1 to 2.0 ml. An industrial filling system was used to acquire data under controlled conditions, using calibrated weighing equipment. To the best of our knowledge, this is the first dataset documenting pump behavior across such a wide range of volumes. The dataset aims to constitute a solid tool that enables investigation from short-term precision to long-term stability standpoints, providing detailed insights into peristaltic pump behavior under various operating conditions. Additionally, the dataset incorporates compensation outcomes across multiple volumes, documenting both statistical and AI-based compensation strategies, thus exemplifying how the statistical behavior of the dosing can change in response to some compensation strategies aimed to improve dosing accuracy. This resource directly addresses pharmaceutical industry needs by supporting optimization of quality control systems and validation of novel compensation strategies.

Indexed as

Drug IndustryInfusion PumpsQuality Control

Identifiers

PMID41053171
PMCPMC12500925

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.