ANDS Logo

Software is available at:

http://sourceforge.net/projects/giiaf-micro-lib

Programming language(s):

python

Project Members:

Andrew Lewis (Project Manager, andrew.lewis@griffith.edu.au)

Heidi Perrett (Developer, h.perrett@griffith.edu.au)

Amanda Miotto (Developer, a.miotto@griffith.edu.au)

Adrian Meedeniya (Research Officer, )

Joanne Morris (Data Source Administrator, j.morris@griffith.edu.au)

ANDS Contact:

Andrew White (Andrew.White@ands.org.au)

Adult Stem

Griffith University

Project Description:

"To develop a software system to centralise the management of a large volume of microscopy image and related experimental metadata, allowing researchers within the National Centre for Adult Stem Cell Research (""NCASCR"") to more effectively organise and analyse their biological imaging experiments.
Objectives of the project are to capture metadata from images generated from microscopy instruments, Import images in a standard format, allow web based browsing and management of image collections capabilities, share imaging collections, allow annotation of images and image collections, searching of metadata and it will generate and send ""published"" image collection metadata to Griffith's Research Activity Hub

The ANDS data capture project is in the area of image data collections, specifically in the fields of neuroscience and adult stem cell therapies.

The project is critical in the fields of neuroscience and adult stem cell therapies due to their heavy dependence on imaging as an experimental readout. Anatomical investigations that are dependent, almost exclusively, on imaging have remained the mainstay in these fields. Importantly, other experimental methodologies applied to these fields such as physiology, pharmacology and molecular biology, which were traditionally independent of imaging are now turning to imaging as experimental readouts.

The explosive increase of imaging in the fields of neuroscience and cell therapies has been driven by several technological advances. The increasing precision of probes for targeting static and dynamic processes within biological systems, together with probe stability, has been paralleled by significant advances in probe detection capabilities.
Maximum utility on these advances has been gained by the computing revolution, which has facilitated rapid acquisition of high-resolution data, its storage and analysis.
These capabilities have provided for major advances in scientific discovery. However, it has also accentuated an existent problem to an unprecedented scale, namely, that of data management for effective access. Image data that was captured in an analogue format a decade ago is now captured exclusively as digital images. Several thousand images frequently comprise a single data set towards an anatomical publication. The imaging of living systems at high resolution for the study of cellular functions may involve the capture of 2000 frames per minute over extended durations.

The large image data collections that frequently underlie biomedical research publications need embedded metadata and storage in appropriate catalogues to allow retesting and mining, to maximize value. This project was perfectly placed to develop capability in this area.
Our objective was to develop a data capture program that would recognize and archive image data derived from a variety of imaging instrumentation and thereby of varied format and metadata content.

The data was generated using a variety of microscopes (wide-field and laser scanning confocal microscopes), as are frequently used in the fields of neuroscience and stem cell biology. As the data was published, they contained minimum prerequisite features that formed the backbone of the metadata. The multiple researchers who contributed this data were asked to provide further annotation of the data through an interphase designed by the project. Early development involved a series of meetings between the ICT and the biomedical researchers, and an evaluation and feedback protocol allowed the fine-tuning of the program.

Image data collections captured on lab or facility-based imaging instruments are largely handled by the researchers or group heads and archived using a variety of modalities. The collections are frequently catalogued in laboratory books and archived on external hard drives or on institute servers. Following publication, the occasional data set may be made available to the wider community as a requirement of the journal. Unlike in fields such as genomics (e.g. Human and mouse genome project), little effort has been made to provide access to published data for public scrutiny and re-analysis.

Thus, providing access to image data collections will be of major benefit to the greater research community.

The large volume of the image data sets and the multidimensionality of its content due to the modern technologies often enable the derivation of multiple features from a given data set. Following publication of the data, the ability to search for, access and re-investigate the data would therefore be useful for multiple fields. E.g. an image data collection for investigating the incidence of a sub-cellular inclusion that occurs in a particular neurodegenerative condition may, in a later investigation be used to define the gross cellular structural changes which may be associated with the condition. The availability of such data, appropriately annotated, will be immensely useful to the field and is likely to be successful when employed by the wider research community.

Through greater access to the wider community, the research finding will be validated through scrutiny. The data may provide further research outcomes at relatively minimal cost and facilitate research collaborations.

Ten published data collections were used for the development of the project. We will translate this to all our published image data collections. Similarly, we will encourage all researchers within the university, registered to use imaging instruments, to make their unencumbered image data collections available to the wider community, following publication.

There are no restrictions on the published data collections.
"

Data Type:

Microscopy images of animal and/or human tissues

High Level Software Functionality:

Features: "a. Captures metadata from microscopy images generated on various microscopy instruments (see instrument list attached)
b. Imports microscopy images in a standard format (OME-XML and/or OME-TIFF) and maintains provenance of metadata and image data
c. Allows users to browse and manage their image collections using a web-based interface
d. Allows users to share their image collections
e. Allows users to annotate their images and image collections
f. Allows users to search image collections on metadata attributes
g. Allows users to flag images and/or image collections for publication
h. Generates and sends ?published? image collection metadata (RIF-CS) to Griffith's Metadata Exchange hub for publication to RDA
i. Automates backup and archiving of image data and metadata
j. Serves published images and image collections for external (open) access either independently or consistent with image collection URIs provided to RDA
";
Download link: http://sourceforge.net/projects/giiaf-micro-lib/files/;

ANZSRC-FOR code:

11 MEDICAL AND HEALTH SCIENCES