Evidence map›Paper›PMID 39324014›Full record

ArticleSouth African journal of science

Data sharing: A Long COVID perspective, challenges, and road map for the future.

Sunday O Oladejo, Liam R Watson, Bruce W Watson, Kanshukan Rajaratnam, Maritha J Kotze, Douglas B Kell, Etheresia Pretorius

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in South African journal of science. 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

7 authors.

Sunday O OladejoSchool for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID 0000-0001-9573-5189
Liam R WatsonSchool for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID 0000-0002-7016-9229
Bruce W WatsonSchool for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID 0000-0003-0511-1837
Kanshukan RajaratnamSchool for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.ORCID 0000-0001-5916-2723
Maritha J KotzeDivision of Chemical Pathology, Department of Pathology, National Health Laboratory Service, Tygerberg Hospital & Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.ORCID 0000-0002-6050-2876
Douglas B KellDepartment of Biochemistry and Systems Biology, Faculty of Health and Life Sciences, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.ORCID 0000-0001-5838-7963
Etheresia PretoriusDepartment of Biochemistry and Systems Biology, Faculty of Health and Life Sciences, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.ORCID 0000-0002-9108-2384

Funding

Research for Ethical Data Science in Southern Africa (REDSSA)U01MH127704 · NIMH · STELLENBOSCH UNIVERSITY · PI MOODLEY, KEYMANTHRI, RENNIE, STUART · 2021 to 2024
$1.6M
NIMH NIH HHS U01 MH127704
6 · The paper itself

Abstract

'Long COVID' is the term used to describe the phenomenon in which patients who have survived a COVID-19 infection continue to experience prolonged SARS-CoV-2 symptoms. Millions of people across the globe are affected by Long COVID. Solving the Long COVID conundrum will require drawing upon the lessons of the COVID-19 pandemic, during which thousands of experts across diverse disciplines such as epidemiology, genomics, medicine, data science, and computer science collaborated, sharing data and pooling resources to attack the problem from multiple angles. Thus far, there has been no global consensus on the definition, diagnosis, and most effective treatment of Long COVID. In this work, we examine the possible applications of data sharing and data science in general with a view to, ultimately, understand Long COVID in greater detail and hasten relief for the millions of people experiencing it. We examine the literature and investigate the current state, challenges, and opportunities of data sharing in Long COVID research.

Indexed as

data sciencedata sharingLong COVID

Identifiers

PMID39324014
PMCPMC11423650

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.