Pioneering Open Science in Bangladesh: A call to action for Data Article adoption
Authors
- M Mostafa ZamanDepartment of Public Health and Informatics, Bangabandhu Sheikh Mujib Medical University (currently, Bangladesh Medical University), Dhaka, Bangladesh https://orcid.org/0000-0002-1736-1342
- Tanvir C TurinDepartment of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada https://orcid.org/0000-0002-7499-5050
- Md Shahinul AlamDepartment of Hepatology, Bangabandhu Sheikh Mujib Medical University (currently, Bangladesh Medical University), Dhaka, Bangladesh https://orcid.org/0000-0001-8053-0738
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Published by Bangabandhu Sheikh
Mujib Medical University (currently, Bangladesh Medical University).
Publishing Data Articles is still relatively underutilised, despite the presence of more than 200 dedicated journals [5]. A few journals began this journey long ago. These articles are peer-reviewed publications that exclusively describe datasets, detailing their structure, collection methodologies, and reuse potential without hypothesis testing (such as using statistical tests typically employed in conventional analytical studies) or novel interpretation. Crucially, they transform raw data into FAIR-aligned [6] scholarly assets by providing comprehensive contextual metadata that exceeds standalone repository documentation. This is especially important for the secondary data due to the concern exist regarding their quality, completeness, preservation, sensitivity/specificity, and reusability.
Datasets published through Data Articles are increasingly being recognised as scholarly products [7]. These are citable publications conferring academic credits to authors. While distinct from analytical research papers, these peer-reviewed papers provide essential contextual metadata that exceeds standalone repository records, enhancing reproducibility and reuse potential. To ensure ethical compliance and FAIR alignment, datasets must be rigorously anonymised, deposited in certified repositories, and shared under open licenses.
A. The anonymised dataset and metadata must be deposited in a certified repository and made available for reuse under a standard open licence (e.g., CC BY 4.0) and DOI [4]. Manuscripts must include a permanent repository link. Trusted data repositories (such as Figshare, Mendeley Data, Zenodo, Harvard Dataverse) need to be used. In the long run, BSMMUJ shall create its own repository.
B. A Data Article should include the following components:
1. The Introduction should provide the background and introduce the dataset. It should justify the publication of such a dataset with a confocal objective.
2. The Methods should describe the acquisition of data, processing/cleaning procedures, its completeness, accuracy, limitations, and accessibility. Any transformation of data (e.g., the creation of new or dummy variables) should be described. Any related articles already published from the dataset should be mentioned.
3. The Findings should be limited to descriptive summary only (e.g., frequencies, distributions) using ≤5 tables/ figures. Inferential statistics should be avoided.
4. The Discussion should focus on the value of the data, comparing and contrasting similar datasets. However, interpretation and conclusion should be avoided. Data limitations and potential areas for improvement should be identified. Finally, comments may be made regarding the utility of the dataset without drawing any conclusions.
Categories | Number (%) |
Sex |
|
Male | 36 (60.0) |
Female | 24 (40.0) |
Age in yearsa | 8.8 (4.2) |
Education |
|
Pre-school | 20 (33.3) |
Elementary school | 24 (40.0) |
Junior high school | 16 (26.7) |
Cancer diagnoses |
|
Acute lymphoblastic leukemia | 33 (55) |
Retinoblastoma | 5 (8.3) |
Acute myeloid leukemia | 4 (6.7) |
Non-Hodgkins lymphoma | 4 (6.7) |
Osteosarcoma | 3 (5) |
Hepatoblastoma | 2 (3.3) |
Lymphoma | 2 (3.3) |
Neuroblastoma | 2 (3.3) |
Medulloblastoma | 1 (1.7) |
Neurofibroma | 1 (1.7) |
Ovarian tumour | 1 (1.7) |
Pancreatic cancer | 1 (1.7) |
Rhabdomyosarcoma | 1 (1.7) |
aMean (standard deviation) |
Categories | Number (%) |
Sex |
|
Male | 36 (60.0) |
Female | 24 (40.0) |
Age in yearsa | 8.8 (4.2) |
Education |
|
Pre-school | 20 (33.3) |
Elementary school | 24 (40.0) |
Junior high school | 16 (26.7) |
Cancer diagnoses |
|
Acute lymphoblastic leukemia | 33 (55) |
Retinoblastoma | 5 (8.3) |
Acute myeloid leukemia | 4 (6.7) |
Non-Hodgkins lymphoma | 4 (6.7) |
Osteosarcoma | 3 (5) |
Hepatoblastoma | 2 (3.3) |
Lymphoma | 2 (3.3) |
Neuroblastoma | 2 (3.3) |
Medulloblastoma | 1 (1.7) |
Neurofibroma | 1 (1.7) |
Ovarian tumour | 1 (1.7) |
Pancreatic cancer | 1 (1.7) |
Rhabdomyosarcoma | 1 (1.7) |
aMean (standard deviation) |
Characteristic | Mild (n=187) | Moderate (n=358) | Severe (n=113) |
Age | |||
20s | 65 (34.8) | 105 (29.3) | 44 (38.9) |
30s | 122 (65.2) | 53 (70.7) | 269 (61.1) |
Sex | |||
Men | 85 (45.5) | 199 (55.6) | 63 (55.8) |
Women | 102 (54.5) | 159 (44.4) | 50 (44.2) |
Smoking | |||
No | 139 (74.3) | 282 (78.8) | 94 (83.2) |
Yes | 48 (25.7) | 76 (21.2) | 19 (16.8) |
Frequency of smoking | |||
Daily | 40 (21.4) | 56 (15.6) | 12 (10.6) |
Non-daily | 8 (4.3) | 20 (5.6) | 7 (6.2) |
Method of smoking | |||
Cigarette | 18 (9.6) | 35 (9.8) | 10 (8.8) |
Hookah | 20 (10.7) | 23 (6.4) | 3 (2.7) |
Both | 10 (5.3) | 18 (5.0) | 6 (5.3) |
aNone of these variables were significantly different between pain categories |
We acknowledge the use of Perplexity.ai to assist with English language editing to improve sentence structure, grammar, and vocabulary for greater clarity. All generated suggestions were critically reviewed and revised by us to ensure the reliability, precision, and integrity of the manuscript. We take full responsibility for the content of this editorial.