HighlightsHCS data management is challenging due to its scale, complexity and heterogeneity.OMERO and Bio-Formats are open-source tools for data access and management at scale.OMERO and Bio-Formats can handle images, experimental metadata and analytic outputs.Repositories integrating multiple image-based studies provide tests of the value of data integration.

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.

Original papers are invited on all aspects of the processing and analysis of medical, small animal, or cellular images, with applications in medicine, biological, and pharmaceutical research. Of interest are algorithms applied to all imaging modalities, including x-ray, DSA, CT, MRI, neuroimaging, nuclear medicine, optical, ultrasound, macroscopic, and microscopic imaging. Papers dealing with the challenges of bringing advances in research laboratories into clinical application are particularly welcomed.

BackgroundA key application area of semantic technologies is the fast-developing field of bioinformatics. Sealife was a project within this field with the aim of creating semantics-based web browsing capabilities for the Life Sciences. This includes meaningfully linking significant terms from the text of a web page to executable web services. It also involves the semantic mark-up of biological terms, linking them to biomedical ontologies, then discovering and executing services based on terms that interest the user.ResultsA system was produced which allows a user to identify terms of interest on a web page and subsequently connects these to a choice of web services which can make use of these inputs. Elements of Artificial Intelligence Planning build on this to present a choice of higher level goals, which can then be broken down to construct a workflow. An Argumentation System was implemented to evaluate the results produced by three different gene expression databases. An evaluation of these modules was carried out on users from a variety of backgrounds. Users with little knowledge of web services were able to achieve tasks that used several services in much less time than they would have taken to do this manually. The Argumentation System was also considered a useful resource and feedback was collected on the best way to present results.ConclusionOverall the system represents a move forward in helping users to both construct workflows and analyse results by incorporating specific domain knowledge into the software. It also provides a mechanism by which web pages can be linked to web services. However, this work covers a specific domain and much co-ordinated effort is needed to make all web services available for use in such a way, i.e. the integration of underlying knowledge is a difficult but essential task.

Access to primary research data is vital for the advancement of the scientific enterprise. It facilitates the validation of existing observations and provides the raw materials to build new hypotheses and make new discoveries. In the life sciences, research communities have repeatedly collaborated to build resources that allow for submission, archiving and access to gene sequences, macromolecular structures, and data from functional genomics experiments. Added value databases build on these archives by harmonising and integrating different datasets to enable simple queries and to unravel underlying biology. To extend the range of data types supported by community repositories, we have built a prototype Image Data Resource (IDR) that collects and integrates imaging data acquired using many different imaging modalities including high-content screening, super-resolution microscopy, timelapse imaging and digital pathology, and links them in a single resource. IDR links experimental perturbations to public genetic or chemical databases, and cell and tissue phenotypes to controlled vocabularies expressed as ontologies. By integrating the phenotypic and genetic metadata from multiple studies, IDR makes it possible to reveal novel functional networks of genetic interactions linked to specific cell phenotypes. To enhance the access to IDR's integrated datasets, we have built a computational resource based on IPython notebooks that allows remote access to the full complement of IDR data. IDR is built as a platform that others can use to publish their own image data, and to enhance and extend the sharing and re-analysis of scientific image data.