Literature DB >> 27342384

Use FlowRepository to share your clinical data upon study publication.

Josef Spidlen1, Ryan R Brinkman1,2.   

Abstract

A fundamental tenet of scientific research is that published results including underlying data should be open to independent validation and refutation. Data sharing encourages collaboration, facilitates quality and reduces redundancy in data production. Authors submitting manuscripts to several journals have already adopted the habit of sharing their underlying flow cytometry data by deposition to FlowRepository-a data repository that is jointly supported by the International Society for Advancement of Cytometry, the International Clinical Cytometry Society and the European Society for Clinical Cell Analysis. De-identification is required for publishing data from clinical studies and we discuss ways to satisfy data sharing requirements and patient privacy requirements simultaneously. Scientific communities in the fields of microarray, proteomics, and sequencing have been benefiting from reuse and re-exploration of data in public repositories for over decade. We believe it is time that clinicians follow suit and that de-identified clinical data also become routinely available along with published cytometry-based findings.
© 2016 International Clinical Cytometry Society. © 2016 International Clinical Cytometry Society.

Entities:  

Keywords:  data availability; data repository; data sharing; de-identification; privacy; reproducible research

Mesh:

Year:  2016        PMID: 27342384     DOI: 10.1002/cyto.b.21393

Source DB:  PubMed          Journal:  Cytometry B Clin Cytom        ISSN: 1552-4949            Impact factor:   3.058


  3 in total

1.  Guidelines for standardizing T-cell cytometry assays to link biomarkers, mechanisms, and disease outcomes in type 1 diabetes.

Authors:  Jennie H M Yang; Kirsten A Ward-Hartstonge; Daniel J Perry; J Lori Blanchfield; Amanda L Posgai; Alice E Wiedeman; Kirsten Diggins; Adeeb Rahman; Timothy I M Tree; Todd M Brusko; Megan K Levings; Eddie A James; Sally C Kent; Cate Speake; Dirk Homann; S Alice Long
Journal:  Eur J Immunol       Date:  2022-01-28       Impact factor: 5.532

2.  High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy.

Authors:  Carsten Krieg; Malgorzata Nowicka; Silvia Guglietta; Sabrina Schindler; Felix J Hartmann; Lukas M Weber; Reinhard Dummer; Mark D Robinson; Mitchell P Levesque; Burkhard Becher
Journal:  Nat Med       Date:  2018-01-08       Impact factor: 87.241

Review 3.  Critical Steps in Data Management During a Crisis.

Authors:  Michele Black; Karla Moncada; Kyle Herstad
Journal:  Cytometry A       Date:  2020-11-24       Impact factor: 4.714

  3 in total

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