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A multidisciplinary journal to publish open datasets for analysis and re-analysis
"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Data can be from already published studies (which should be cited or even included), or unpublished data which has not yet been analyzed but may be useful for others.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
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.
Background: Different studies have suggested that web search data is useful in forecasting several phenomena from the field of economics to epidemiology or health issues. Objective: The objectives of...
Background: Different studies have suggested that web search data is useful in forecasting several phenomena from the field of economics to epidemiology or health issues. Objective: The objectives of this study are (1) to evaluate the correlation between suicidal rates released by the Spanish National Statistics Institute (INE) and Internet searches trends in Spain reported by Google Trends (GT) for 57 suicide-related terms representing major known risks of suicide that have been already tested in previous scientific studies systematized by Mok et al. , that correspond to epigraph 1.1 of Table 1 and are referenced in Table 2. The period of our study was from 2004 to 2013 by the availability of data from both the INE and GT. And (2) to study the differential association between male and female suicide rates published by the INE and Internet searches of these 57 terms. Methods: This study collected suicide data from (1) Spain’s INE and (2) local Internet search data from GT, both from January 2004 to December 2013. We investigated and validated fifty seven suicide-related terms already tested in scientific studies previous to 2015 that would be the best predictors of new suicide cases. We then evaluated the nowcasting effects of GT search through cross-correlation analysis and by linear regression of the suicide incidence data with the GT data. Results: Suicide rates for that period in Spain were positively associated for general population with the search volume for six terms and negatively for one from the total of fifty seven terms used in previous studies. Suicide rates for men were found significantly different from that of women. The search term “allergy” demonstrated a lead effect for new suicide cases (r=.513, P=.000). The next significant correlating terms for those fifty seven studied were “antidepressant”, “alcohol abstinence”, “relationship breakup” (r=.295, P=.001; r=.295, P=.001; r=.268, P=.002, respectively). Significant differenced results were obtained for men and women. Conclusions: A better understanding of Internet search behavior of both men and women in relation to suicide and related topics may help design effective suicide prevention programs based on information provided by search robots and other big-data sources.