Evidence map›Paper›PMID 37938260›Full record

ArticleScientific data2023

Daylong acoustic recordings of grazing and rumination activities in dairy cows.

Luciano S Martinez-Rau, José O Chelotti, Mariano Ferrero, Santiago A Utsumi, Alejandra M Planisich, Leandro D Vignolo, Leonardo L Giovanini, H Leonardo Rufiner, Julio R Galli

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2023. 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
1.9field-weighted citation impact, top 16% of its field
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, 10 citations in OpenAlex.

  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

9 authors at 3 institutions in 4 countries.

Luciano S Martinez-RauInstituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina. luciano.martinezrau@miun.se.ORCID 0000-0002-2336-5390
José O ChelottiInstituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina.
Mariano FerreroInstituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina.ORCID 0000-0002-4298-6440
Santiago A UtsumiW.K. Kellogg Biological Station and Department of Animal Science, Michigan State University, East Lansing, USA.
Alejandra M PlanisichFacultad de Ciencias Agrarias, Universidad Nacional de Rosario, Santa Fe, Argentina.
Leandro D VignoloInstituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina.ORCID 0000-0002-8176-8235
Leonardo L GiovaniniInstituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina.
H Leonardo RufinerInstituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina.ORCID 0000-0002-1083-5891
Julio R GalliFacultad de Ciencias Agrarias, Universidad Nacional de Rosario, Santa Fe, Argentina.
Consejo Nacional de Investigaciones Científicas y Técnicas · ARNational University of Rosario · ARNew Mexico State University · US

Funding

Consejo Nacional de Investigaciones Científicas y Técnicas (National Scientific and Technical Research Council) 22920170100014COUnited States Department of Agriculture | National Institute of Food and Agriculture (NIFA) MICL0222Universidad Nacional del Litoral (National University of the Litoral) CAID 50620190100151LIUniversidad Nacional del Litoral (National University of the Litoral) CIAD 50620190100080LIUniversidad Nacional de Rosario (National University of Rosario) 80020180300053UR
6 · The paper itself

Abstract

Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming.

Indexed as

AcousticsCattleFeeding BehaviorAnimalsFarmsFemaleLactation

Identifiers

PMID37938260
PMCPMC10632420
OpenAlexW4388500174

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.