Orchestrating and sharing large multimodal data for transparent and reproducible research
It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects.
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/da2c696db1874e349ef2975f7942b0cf |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:da2c696db1874e349ef2975f7942b0cf |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:da2c696db1874e349ef2975f7942b0cf2021-12-02T18:37:16ZOrchestrating and sharing large multimodal data for transparent and reproducible research10.1038/s41467-021-25974-w2041-1723https://doaj.org/article/da2c696db1874e349ef2975f7942b0cf2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25974-whttps://doaj.org/toc/2041-1723It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects.Anthony MammolitiPetr SmirnovMinoru NakanoZhaleh SafikhaniChristopher EelesHeewon SeoSisira Kadambat NairArvind S. MerIan SmithChantal HoGangesh BeriRebecca KuskoMassive Analysis Quality Control (MAQC) Society Board of DirectorsEva LinYihong YuScott MartinMarc HafnerBenjamin Haibe-KainsNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Science Q |
| spellingShingle |
Science Q Anthony Mammoliti Petr Smirnov Minoru Nakano Zhaleh Safikhani Christopher Eeles Heewon Seo Sisira Kadambat Nair Arvind S. Mer Ian Smith Chantal Ho Gangesh Beri Rebecca Kusko Massive Analysis Quality Control (MAQC) Society Board of Directors Eva Lin Yihong Yu Scott Martin Marc Hafner Benjamin Haibe-Kains Orchestrating and sharing large multimodal data for transparent and reproducible research |
| description |
It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects. |
| format |
article |
| author |
Anthony Mammoliti Petr Smirnov Minoru Nakano Zhaleh Safikhani Christopher Eeles Heewon Seo Sisira Kadambat Nair Arvind S. Mer Ian Smith Chantal Ho Gangesh Beri Rebecca Kusko Massive Analysis Quality Control (MAQC) Society Board of Directors Eva Lin Yihong Yu Scott Martin Marc Hafner Benjamin Haibe-Kains |
| author_facet |
Anthony Mammoliti Petr Smirnov Minoru Nakano Zhaleh Safikhani Christopher Eeles Heewon Seo Sisira Kadambat Nair Arvind S. Mer Ian Smith Chantal Ho Gangesh Beri Rebecca Kusko Massive Analysis Quality Control (MAQC) Society Board of Directors Eva Lin Yihong Yu Scott Martin Marc Hafner Benjamin Haibe-Kains |
| author_sort |
Anthony Mammoliti |
| title |
Orchestrating and sharing large multimodal data for transparent and reproducible research |
| title_short |
Orchestrating and sharing large multimodal data for transparent and reproducible research |
| title_full |
Orchestrating and sharing large multimodal data for transparent and reproducible research |
| title_fullStr |
Orchestrating and sharing large multimodal data for transparent and reproducible research |
| title_full_unstemmed |
Orchestrating and sharing large multimodal data for transparent and reproducible research |
| title_sort |
orchestrating and sharing large multimodal data for transparent and reproducible research |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/da2c696db1874e349ef2975f7942b0cf |
| work_keys_str_mv |
AT anthonymammoliti orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT petrsmirnov orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT minorunakano orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT zhalehsafikhani orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT christophereeles orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT heewonseo orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT sisirakadambatnair orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT arvindsmer orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT iansmith orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT chantalho orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT gangeshberi orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT rebeccakusko orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT massiveanalysisqualitycontrolmaqcsocietyboardofdirectors orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT evalin orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT yihongyu orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT scottmartin orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT marchafner orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch AT benjaminhaibekains orchestratingandsharinglargemultimodaldatafortransparentandreproducibleresearch |
| _version_ |
1718377831051821056 |