Evidence map›Paper›PMID 41331296›Full record

ArticleScientific data2025

How does academia recognize the contribution of scientific data? Evidence from data contributors' authorship.

Jiaxue Liu, Xiaowei Ma, Hong Jiao, Yuhong Qiu, Tong Niu, Bo Yang

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

6 authors.

Jiaxue LiuCollege of Information Management, Nanjing Agricultural University, Nanjing, 210095, P. R. China.
Xiaowei MaCollege of Information Management, Nanjing Agricultural University, Nanjing, 210095, P. R. China.ORCID http://orcid.org/0000-0002-6218-5448
Hong JiaoCollege of Information Management, Nanjing Agricultural University, Nanjing, 210095, P. R. China.
Yuhong QiuLibrary of Hangzhou City University, Hangzhou, 310015, P. R. China.
Tong NiuCollege of Information Management, Nanjing Agricultural University, Nanjing, 210095, P. R. China.
Bo YangCollege of Information Management, Nanjing Agricultural University, Nanjing, 210095, P. R. China. mail.boyang@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scientific data has become a cornerstone of contemporary biomedical research, yet the academic recognition of data contributions remains underexplored. In this study, we leveraged the open-access biomedical literature in the PMC (PubMed Central) to identify GEO (Gene Expression Omnibus) datasets and extract their associated original papers. By examining the authorship relationships between dataset contributors and paper authors, we quantitatively assessed the academic recognition of scientific data. Our findings reveal that approximately 80% of dataset contributors play pivotal roles in their respective original papers, either as first or corresponding authors, with this proportion continuing to rise. This trend highlights the growing importance of data collection, processing, and analysis in the research process, along with its increasing recognition by the scientific community. Furthermore, we observed that high-impact journals invest more resources in enhancing data quality, thereby improving research credibility, academic influence, and overall research outcomes. These results underscore the gradual shift toward recognizing the value of scientific data work, which is critical for advancing research quality.

Indexed as

AuthorshipBiomedical ResearchAcademia

Identifiers

PMID41331296
PMCPMC12800197

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.