JMIR Data

A multidisciplinary journal to publish open datasets for analysis and re-analysis.

Editor-in-Chief:

Gunther Eysenbach, MD, MPH, FACMI, Founding Editor and Publisher; Adjunct Professor, School of Health Information Science, University of Victoria, Canada


"The future is data" (Patty Brennan, Incoming NLM Director, 2016).

 

Do you have a dataset that has already been analyzed and led to a number of publications, but which could also be valuable for other researchers? Do you want to get credit for generating a potentially interesting dataset even if you are not the one who wish to analyze them? Do you wish to launch a challenge / competition (you have a dataset, and want others to solve a problem or answer a question with it - can be combined with publishing a competition document in JMIR Challenges).

JMIR Data is a new unique journal focusing on the publication and curation of datasets, small and large, in the field of medicine and health.

This can include - but is not limited to - molecular and genomic data, patient/participant data from trials and experiments (properly anonymized), patient-generated data (e.g. from accelerometers/mobile apps) or patient-reported outcomes, unstructured data such as interview/focus group transscripts, "big data" from a variety of data sources, or unusual data such as Internet log files or app usage files.

Data can be from already published studies (which should be cited or even included), or unpublished data which has not yet been analyzed but may be useful for others.  

Data files can be provided as excel files, SQL files, or other file formats.

All data files need to be properly anonymized and de-identified to protect the privacy of participants.

Data will be lightly peer-reviewed, with a focus on privacy (can individuals be re-identified?) and on the brief paper accompaniying the dataset.

Each submission consists of a brief paper and the dataset(s) as Mutlimedia Appendix. We also recommend to include other relevant documents such as IRB approvals etc.

 

For the paper we recommend the following structure:

Introduction: What is the history/background/motivation for the dataset / datacollection and the issues to be addressed with the dataset? Possible research questions to be answered with the dataset? Have the research questions been answered already completely or partially or not at all?

Methods: How were the data collected?

Results: Briefly state all results and cite the papers known 

Discussion: For whom is the dataset useful and what remains to be done/analyzed?

Conflict of Interest

Acknowledgments (optional)

Author contributions (optional) 

Multimedia Appendix: All data files, as well as IRB approval / informed consent forms etc., if applicable, and PDFs of publications (if published under a Creative Commons license)

 

In addition to publishing brief papers with datasets, we will also publish viewpoint papers, tutorials and reviews related to collection of datasets, ethics and privacy (e.g. de-identification methods), file formats and curation of datasets.

Recent Articles

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Multiple lines of previous research have documented that author conflicts of interest (COI) can compromise the integrity of the biomedical research enterprise. However, continuing research that would investigate why, how, and in what circumstances COI is most risky is stymied by the difficulty in accessing disclosure statements, which are not widely represented in available databases.

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Depression detection in social media has gained attention in recent years with the help of natural language processing (NLP) techniques. Because of the low-resource standing of Filipino depression data, valid data sets need to be created to aid various machine learning techniques in depression detection classification tasks.

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The Joint United Nations Programme on HIV/AIDS has set targets for 2025 regarding people living with HIV. For these targets to be met, 95% of people with HIV would need to know their HIV status, 95% of people with HIV would need to be receiving antiretroviral therapy, and 95% of people on antiretroviral therapy would need to be virally suppressed. Some countries are on track to meet these targets. However, within and across countries, several vulnerable populations may not meet these targets. This is in part because several approaches to improving the cascade of care after an HIV diagnosis are not tailored to and are not appropriate for vulnerable populations, such as men who have sex with men, sex workers, people who inject drugs, Black people, people in prisons, women, and youth. To inform research, policy, and practice, there is a need for curated data on HIV care cascade research.

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The COVID-19 pandemic has had a substantial impact on economies, governments, businesses, and most importantly, people’s health. To bring the spread of COVID-19 under control, strict lockdown measures have been implemented across the globe. These lockdown measures resulted in a spate of panic buying and increase in demand for hygiene products and other grocery items.

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Blood pressure (BP) is an important marker for cardiovascular health. However, a person’s BP data cannot usually be obtained simultaneously from different sources.

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Preprints Open for Peer-Review

There are no preprints available for open peer-review at this time. Please check back later.

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