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1.
Nat Methods ; 15(11): 984, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30287931

RESUMO

This paper was originally published under standard Nature America Inc. copyright. As of the date of this correction, the Resource is available online as an open-access paper with a CC-BY license. No other part of the paper has been changed.

2.
Nat Methods ; 14(8): 775-781, 2017 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28775673

RESUMO

Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR) that collects and integrates imaging data acquired across many different imaging modalities. IDR links data from several imaging modalities, including high-content screening, super-resolution and time-lapse microscopy, digital pathology, public genetic or chemical databases, and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable re-analysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open source platform that others can use to publish their own image data. Thus IDR provides both a novel on-line resource and a software infrastructure that promotes and extends publication and re-analysis of scientific image data.


Assuntos
Sistemas de Gerenciamento de Base de Dados , Bases de Dados Factuais , Interpretação de Imagem Assistida por Computador/métodos , Disseminação de Informação/métodos , Software , Interface Usuário-Computador , Algoritmos , Editoração , Integração de Sistemas
3.
Methods ; 96: 27-32, 2016 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-26476368

RESUMO

High content screening (HCS) experiments create a classic data management challenge-multiple, large sets of heterogeneous structured and unstructured data, that must be integrated and linked to produce a set of "final" results. These different data include images, reagents, protocols, analytic output, and phenotypes, all of which must be stored, linked and made accessible for users, scientists, collaborators and where appropriate the wider community. The OME Consortium has built several open source tools for managing, linking and sharing these different types of data. The OME Data Model is a metadata specification that supports the image data and metadata recorded in HCS experiments. Bio-Formats is a Java library that reads recorded image data and metadata and includes support for several HCS screening systems. OMERO is an enterprise data management application that integrates image data, experimental and analytic metadata and makes them accessible for visualization, mining, sharing and downstream analysis. We discuss how Bio-Formats and OMERO handle these different data types, and how they can be used to integrate, link and share HCS experiments in facilities and public data repositories. OME specifications and software are open source and are available at https://www.openmicroscopy.org.


Assuntos
Biologia Computacional/estatística & dados numéricos , Mineração de Dados/estatística & dados numéricos , Ensaios de Triagem em Larga Escala/estatística & dados numéricos , Armazenamento e Recuperação da Informação/estatística & dados numéricos , Software , Biologia Computacional/métodos , Conjuntos de Dados como Assunto , Ensaios de Triagem em Larga Escala/métodos , Humanos , Disseminação de Informação , Armazenamento e Recuperação da Informação/métodos , Internet
4.
Mamm Genome ; 26(9-10): 441-7, 2015 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-26223880

RESUMO

Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO's Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org.


Assuntos
Disseminação de Informação , Imagem Molecular , Software , Animais , Internet , Editoração
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