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Make use of Open Data!

Open data sharing to increase robustness and value in biomedical research is becoming increasingly common. It is requested by many funders and journals, and the Charité has signed the Berlin Declaration, which endorses the open sharing of research data. The rapidly growing amount of openly available data has also spurred the development of dataset search engines, which allow the location of relevant datasets. Corresponding to this development, the number of publications based on secondary data analysis, reusing open data, is also on the rise. However, it is still uncommon in many areas of biomedical research to base a study on secondary analysis of raw data. Thus, the QUEST Open Data Reuse award rewards publications which made use of publicly available datasets. The goal is to raise awareness for the importance of data as first-class research outcomes and the potential that data reuse has for contributing to innovation, interdisciplinarity and scientific progress.

The following criteria must be met for a publication to be eligible:

  • The publication is predominantly based on reused data.
  • The data were not generated by any of the co-authors (except if the collaboration itself was sparked by the respective co-author(s) sharing the reused datasets).
  • The data used might be raw or pre-processed, but must be at a level of granularity substantially exceeding summary statistics.
  • The majority of data was provided openly via public repositories, which can be all-purpose, disciplinary, or institutional repositories; thus, conversely, publications are not eligible if data in their majority were shared personally, for pay, or under a data sharing agreement or other usage restrictions.
  • The dataset(s) used are properly acknowledged.

QUEST is giving away this award of 1,000 € to first/last/corresponding authors (BIH or Charité affiliation) of preclinical or clinical research papers published from 2017 onwards. Each paper can only receive one of the QUEST awards, and every researcher may apply for each award only once. Publications of authors (independent of the author’s position) which are also QUEST employees cannot be considered.

The award (travel costs or consumables) will be administered through BIH. Prizes can be spent until the end of 2021. Awards are given on an ongoing basis. Awardees will be featured on the QUEST web pages.

To claim your award, send an email with a short statement and the publication as attachments to quest@bihealth.de.

Publications of authors (independent of the author´s position) which are also QUEST employees cannot be considered.



  1. The publication "Pulmonary dysfunction and development of different cardiovascular outcomes in the general population" receives an Open Data Reuse Award. The publication is completely based on openly available data from the English Longitudinal Study of Ageing (ELSA). The authors used these data to investigate the pathobiology involved in the co-development of pulmonary dysfunction and cardiovascular disease. The article was published in 2018 in Archives of Cardiovascular Diseases (DOI: 10.1016/j.acvd.2017.07.001). Applicant: Dr. Bob Siegerink, Centrum für Schlaganfallforschung Berlin (CSB), Charité - Universitätsmedizin Berlin.
  2. The publication "CADD: predicting the deleteriousness of variants throughout the human genome" receives an Open Data Reuse Award. The publication describes the newest version of “Combined Annotation Dependent Depletion”, a tool for assessing human genomic variants. For its improvement, data from over 40 publicly available sources have been used, e.g. EBI ENSEMBL, ENCODE, and gnomAD. CADD is freely available online, with all code and data underlying it. The article was published in 2019 in Nucleic Acids Research (DOI: 10.1093/nar/gky1016). Applicant: Philipp Rentzsch, Computational Genome Biology, Berlin Institute of Health and Charité – Universitätsmedizin Berlin.
  3. The publication "A Computational Analysis of Alternative Splicing across Mammalian Tissues Reveals Circadian and Ultradian Rhythms in Splicing Events" receives an Open Data Reuse Award. The authors carried out a systematic genome-wide evaluation of circadian and ultradian rhythms in transcription, focusing on the post-transcriptional event of alternative splicing. The analysis was primarily based on two freely available circadian datasets of mammalian tissues, shared via GEO and ENA databases, respectively. The article was published in 2019 in International Journal of Molecular Sciences (DOI: 10.3390/ijms20163977). Applicant: Rukeia El-Athman, Molecular Cancer Research Center (MKFZ), Charité - Universitätsmedizin Berlin
  4. The publication “Evaluating the replicability, specificity, and generalizability of connectome fingerprints” receives an Open Data Reuse Award. The authors showed that the accuracy reported for the detection of unique brain signatures can be replicated, but that this does not necessarily mean a high specificity, especially when standard-quality neuroimaging data and larger datasets are used. For this, they used an openly available Human Connectome Project dataset. The article was published in 2017 in NeuroImage (DOI: 10.1016/j.neuroimage.2017.07.016). Applicant: Lea Waller, Research Division of Mind & Brain, Charité - Universitätsmedizin Berlin
  5. The publication “Antidepressants and suicidality: A re-analysis of the re-analysis” receives an Open Data Reuse Award. The authors showed that the conclusions of earlier analyses on suicidality with antidepressants might be simplified and that the outcome strongly depends on the statistical analysis method used. For their re-analysis the authors used data available in the Drugs@FDA database. The article was published in 2020 in the Journal of Affective Disorders (DOI: 10.1016/j.jad.2020.01.107). Applicant: Jakob André Kaminski, Klinik für Psychiatrie und Psychotherapie (CCM), Charité – Universitätsmedizin Berlin
  6. The publication “On the treatment effect heterogeneity of antidepressants in major depression. A Bayesian meta-analysis” receives an Open Data Reuse Award. The authors found that the published RCT data on antidepressants are compatible with a near-constant treatment effect, and thus the hope for personalized antidepressant treatments may be unwarranted. The analysis was based on a dataset of RCTs on antidepressants shared through Mendeley. The article was published in 2020 as a preprint on medRxiv (DOI: 10.1101/2020.02.20.19015677). Applicant: Constantin Volkmann, Klinik für Psychiatrie und Psychotherapie, Charité – Universitätsmedizin Berlin
  7. The publication “A unified connectomic target for deep brain stimulation in obsessive-compulsive disorder” receives an Open Data Reuse Award. The authors established that different deep brain stimulation targets used to treat obsessive-compulsive disorder act by activating the same neural network. For this, they combined observations from own experiments with a normative connectome derived from an open database provided by the Human Connectome Project. The article was published in 2020 in Nature Communications (DOI: 10.1038/s41467-020-16734-3). Applicant: Ningfei Li, Department of Neurology, Movement Disorders and Neuromodulation Section, Charité - Universitätsmedizin Berlin
  8. The publication "Distinct immune evasion in APOBEC-enriched, HPV-negative HNSCC" receives a QUEST Open Data Reuse Award. The authors identified a new subgroup of head and neck squamous cell carcinoma (HNSCC) with a distinct immunogenic phenotype, which could potentially mediate responses to immunotherapy. The findings were predominantly based on openly available datasets, accessible through The Cancer Genome Atlas and Gene Expression Omnibus. The article was published in 2020 in the journal "International Journal of Cancer" (DOI: 10.1002/ijc.33123). Applicant: Damian Rieke, Medizinische Klinik m. S. Hämatologie, Onkologie und Tumorimmunologie (CBF), Charité – Universitätsmedizin Berlin
  9. The publication "Using Bayes theorem to estimate positive and negative predictive values for continuously and ordinally scaled diagnostic tests" receives a QUEST Open Data Reuse Award. The author showed that using a Bayesian framework, the positive and negative predictive values for a depression diagnosis can be improved as compared to dichotomous scores. The findings were based on openly available datasets, accessible through the Harvard Dataverse repository. The article was published in 2021 in the journal "International Journal of Methods in Psychiatric Research" (DOI: 10.1002/mpr.1868). Applicant: Felix Fischer, Department for Psychosomatic Medicine, Clinic for Internal Medicine and Dermatology, Charité – Universitätsmedizin Berlin