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Call for papers - Data sets to advance diabetes

Guest Editors

Daba Abdissa, BSC, MSc, PhD fellow, Jimma University, Ethiopia
Humayun Kabir, BScN, MPH, MSc, McMaster University, Canada

Submission Status: Open   |   Submission Deadline: 29 November 2024

BMC Research Notes is calling for submissions to our Collection on the use of datasets to advance our understanding of diabetes.

Affecting over 422 million people worldwide, diabetes is now a leading cause of human morbidity and mortality, with cases rising at an alarming rate across low and middle-income countries.

Diabetes Mellitus is also a complex health condition influenced as much by biology as it is by psychosocial, economic, and cultural factors. Its understanding requires diverse datasets on the disease, ranging from genomics and proteomics to continuous glucose monitoring, socio-demographic and health records. Collating and integrating datasets on diabetes such as these are foundational to developing effective strategies for implementing targeted interventions and refining personalized approaches to diabetes management.

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health and Well-being. 

Meet the Guest Editors

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Daba Abdissa, BSC, MSc, PhD fellow, Jimma University, Ethiopia

Daba Abdissa has a Bachelor of Science degree in public health from Jimma University and a Master of Science from Jimma University (2020). He is currently a PhD student in health behavior and health communication. He is interested in non-communicable diseases, neurology, and infectious diseases research. He has participated in peer reviews in several international journals and is an editorial board member at BMC Research Notes and BMC Public Health.
 

Humayun Kabir, BScN, MPH, MSc, McMaster University, Canada

Humayun Kabir is a graduate researcher at the Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Canada, and is deeply committed to conducting high-quality research in public health and epidemiology. His academic journey encompasses a Master of Public Health (MPH) from North South University, Dhaka, Bangladesh, and a Bachelor of Science in Nursing (BScN) from Shahjalal University of Science and Technology, Sylhet, Bangladesh. He has expertise in conducting in-depth epidemiological research, statistical modeling, managing large databases, and analyzing data using programming and statistical software such as Python, R, STATA, and SPSS. With comprehensive training in health research methodology and statistical modeling, he is confident in his ability to contribute to designing and executing research that is scientifically rigorous and methodologically sound. His MSc thesis (Health Research Methodology) at McMaster University involves utilizing the Canadian Longitudinal Study in Aging (CLSA) data. He has published over 30 peer-reviewed publications in a variety of international journals, reviewed over 80 articles, and edited over 60.
 

About the Collection

BMC Research Notes invites submissions to a Collection entitled Data sets to advance diabetes.

Affecting over 422 million people worldwide, diabetes is now a leading cause of human morbidity and mortality, with cases rising at an alarming rate across low and middle-income countries.

Diabetes Mellitus is also a complex health condition influenced as much by biology as it is by psychosocial, economic, and cultural factors. Its understanding requires diverse datasets on the disease, ranging from genomics and proteomics to continuous glucose monitoring, socio-demographic and health records. Collating and integrating datasets on diabetes such as these are foundational to developing effective strategies for implementing targeted interventions and refining personalized approaches to diabetes management.

This Collection aims to compile and showcase datasets that hold the potential to drive advancements in diabetes research. 

We invite Data Note articles covering all facets of diabetes research and original research articles investigating:

  • Integration of omics data in diabetes research
  • Personalized medicine approaches in diabetes care
  • Biomarkers for predicting diabetic complications
  • Impact of electronic health records and wearables on diabetes management
  • Population-based Observational and Intervention studies on diabetes epidemiology
  • Machine Learning for developing and validating predictive models, diagnostic tools, and personalized treatment strategies 
  • Continuous glucose monitoring data in glycemic control

This Collection aligns with the United Nations' Sustainable Development Goal 3: Good Health and Well-Being (SDG3) by contributing to the advancement of diabetes research and promoting universal health coverage. By focusing on the power of datasets in diabetes research, we aim to foster equitable access to quality healthcare services, reducing disparities in diabetes care and furthering our understanding of this critical global health challenge.

Image credit: ra2 studio / stock.adobe.com

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Data Note Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process, you will be asked whether you are submitting to a Collection; please select Data sets to advance diabetes from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.