Evidence map›Paper›PMID 36164297›Full record

ArticleData in brief2022

Raw data collected from NO

Pau Ferrer-Cid, Jose M Barcelo-Ordinas, Jorge Garcia-Vidal

Abstract read
In one paragraph

Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Pau Ferrer-CidUniversitat Politecnica de Catalunya (UPC), C/Jordi Girona 31, Barcelona 08034, Spain.
Jose M Barcelo-OrdinasUniversitat Politecnica de Catalunya (UPC), C/Jordi Girona 31, Barcelona 08034, Spain.
Jorge Garcia-VidalUniversitat Politecnica de Catalunya (UPC), C/Jordi Girona 31, Barcelona 08034, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, the monitoring of air pollution by means of low-cost sensors has become a growing research field due to the study of techniques based on machine learning to improve the sensors' data quality. For this purpose, sensors undergo a calibration process, where these are placed in-situ nearby a regulatory reference station. The data set explained in this paper contains data from two self-built low-cost air pollution nodes deployed for four months, from January 16, 2021 to May 15, 2021, at an official air quality reference station in Barcelona, Spain. The goal of the deployment was to have five electrochemical sensors at a high sampling rate of 0.5 Hz; two NO

Indexed as

Air pollutionElectrochemical sensorsLow-cost sensorsNitrogen dioxideNitrogen monoxideSensor calibrationTropospheric ozone

Identifiers

PMID36164297
PMCPMC9508507

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.