Fred Howell works for Textensor and is one of the developers of publicationslist.org.
Our latest service is A.nnotate.com which lets you discuss documents online by attaching notes to highlighted phrases - for online peer review, getting feedback on a draft from co-authors, or indexing the papers you read.
Some earlier work in neuroinformatics is described on the neurogems.org site.
Abstract: Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperability between software systems therefore play an important role in making the modeling process more efficient and in ensuring that published models can be reliably and easily reused. Various forms of interoperability are possible including the development of portable model description standards, the adoption of common simulation languages or the use of standardized middleware. Each of these approaches finds applications within the broad range of current modeling activity. However more effort is required in many areas to enable new scientific questions to be addressed. Here we present the conclusions of the "NeuroÂIT Interoperability of Simulators" workshop, held at the 11th computational neuroscience meeting in Edinburgh (July 19/Â20 2006; www.cnsorg.org). We assess the current state of interoperability of neural simulation software and explore the future directions that will enable the field to advance.
Abstract: The integrative ambitions of systems biology and neuroinformatics - to construct working models of the machinery of living cells and brains - will flounder unless researchers have access to the huge amounts of diverse experimental data being collected. However, the vast majority of bioscience research data that is gathered is never made available to other researchers, partly for the want of an adequate software for annotating experimental data, and partly for social reasons (researchers are rarely rewarded for publishing the actual data sets - just for journal articles summarizing findings).
We have developed a novel software solution aimed at making it simpler for researchers to annotate and publish their research data. The first part of this solution is a desktop application, Catalyzer, which lets researchers structure their data at source, and complements existing ad hoc solutions in use in labs (including cryptic filenames, Word, Excel, paper lab books) while being simpler and more flexible than relational databases, which are too complex for most bioscience researchers to set up. The catalogs produced by Catalyzer are stored in XML with a user defined schema, which will simplify future data mining efforts across large numbers of distributed data sets. The approach can be summarized as structure at source, integrate as required, with the initial focus on enabling the researchers to structure their own research data; only then will other researchers be able to integrate across data sets.
Abstract: One of the main roles of the Neural Open Markup Language, NeuroML, is to facilitate cooperation in
building, simulating, testing and publishing models of channels, neurons and networks of neurons. MorphML,
which was developed as a common format for exchange of neural morphology data, is distributed as part of
NeuroML but can be used as a stand-alone application. In this collection of tutorials and workshop summary, we
provide an overview of these XML schemas and provide examples of their use in down-stream applications. We
also summarize plans for the further development of XML specifications for modeling channels, channel
distributions, and network connectivity.
Abstract: Many areas of biological research generate large volumes of very diverse data. Managing this data can be a difficult and time-consuming process, particularly in an academic environment where there are very limited resources for IT support staff such as database administrators. The most economical and efficient solutions are those that enable scientists with minimal IT expertise to control and operate their own desktop systems. Axiope provides one such solution, Catalyzer, which acts as flexible cataloging system for creating structured records describing digital resources. The user is able specify both the content and structure of the information included in the catalog. Information and resources can be shared by a variety of means, including automatically generated sets of web pages. Federation and integration of this information, where needed, is handled by Axiope's Mercat server. Where there is a need for standardization or compatibility of the structures usedby different researchers this canbe achieved later by applying user-defined mappings in Mercat. In this way, large-scale data sharing can be achieved without imposing unnecessary constraints or interfering with the way in which individual scientists choose to record and catalog their work. We summarize the key technical issues involved in scientific data management and data sharing, describe the main features and functionality of Axiope Catalyzer and Axiope Mercat, and discuss future directions and requirements for an information infrastructure to support large-scale data sharing and scientific collaboration.
Abstract: Neuroscience is generating vast amounts of highly diverse data which is of potential interest to researchers beyond the laboratories in which it is collected. In particular, quantitative neuroanatomical data is relevant to a wide variety of areas, including studies of development, aging, pathology and in biophysically oriented computational modelling. Moreover, the relatively discrete and well-defined nature of the data make it an ideal application for developing systems designed to facilitate data archiving, sharing and reuse. At present, the only widely used forms of dissemination are figures and tables in published papers which suffer from inaccessibility and the loss of machine readability. They may also present only an averaged or otherwise selected subset of the available data. Numerous database projects are in progress to address these shortcomings. They employ a variety of architectures and philosophies, each with its own merits and disadvantages. One axis on which they may be distinguished is the degree of top-down control, or curation, involved in data entry. Here we consider one extreme of this scale in which there is no curation, minimal standardization and a wide degree of freedom in the form of records used to document data. Such a scheme has advantages in the ease of database creation and in the equitable assignment of perceived intellectual property by keeping the control of data in the hands of the experts who collected it. It does, however, require a more sophisticated infrastructure than conventional databases since the software must be capable of organizing diverse and differently documented data sets in an effective way. Several components of a software system to provide this infrastructure are now in place. Examples are presented, showing how these tools can be used to archive and publish neuronal morphology data, and how they can give an integrated view of data stored at many different sites.
Abstract: Biological nervous systems and the mechanisms underlying their operation exhibit astonishing complexity. Computational models of these systems have been correspondingly complex. As these models become ever more sophisticated, they become increasingly difficult to define, comprehend, manage and communicate. Consequently, for scientific understanding of biological nervous systems to progress, it is crucial for modellers to have software tools that support discussion, development and exchange of computational models. We describe methodologies that focus on these tasks, improving the ability of neuroscientists to engage in the modelling process. We report our findings on the requirements for these tools and discuss the use of declarative forms of model description--equivalent to object-oriented classes and database schema--which we call templates. We introduce NeuroML, a mark-up language for the neurosciences which is defined syntactically using templates, and its specific component intended as a common format for communication between modelling-related tools. Finally, we propose a template hierarchy for this modelling component of NeuroML, sufficient for describing models ranging in structural levels from neuron cell membranes to neural networks. These templates support both a framework for user-level interaction with models, and a high-performance framework for efficient simulation of the models.
Abstract: In this paper we explore the use of a collaborative online annotation tool for enhanced teaching and research of music analysis in higher education. Manual annotation of printed scores on paper has long been the foundation of music analysis. However, collaboration between music analysts in different countries and discussions on a specific piece under analysis has always been problematic. Moving the process online offers the possibility for sharing and discussion with a group of students or researchers; more interactions; simpler publication; and a more permanent and readable record of analyses than is possible with paper methods.
A.nnotate.com is an online web2.0 service designed for discussing documents; it lets several people attach notes to highlighted text or figures to a single copy of document using a web browser. Scores can be uploaded in PDF format and annotated in a web browser with no software installation. New comments can be added by highlighting text or particular notes or phrases of the score. Tags / keywords can be added to comments to help classify particular musical phrases; depending on the type of analysis. In this case these can be motivic (e.g. 'theme 1'), harmonic ('C minor'), structural (section A), paradigmatic (class 1), and many more. Other students can reply to any comment, allowing a detailed discussion to occur at a precise point in the score.
To investigate how online annotation can be used as an eLearning tool for teaching music analysis, we ran an analysis experiment using A.nnotate with a class of 16 undergraduate music students at the University of Athens. We created 4 groups of 4 students collaborating on the same score, and instructed them to annotate the primary motives and their transformed occurrences, main sections, as well as major key changes.
We found that the main benefits of online annotation were: ability for students to work together on a score without having to be in the same place; having a permanent and readable record of the analysis; an easier way for students to compare their work with analyses done by the lecturer and other groups of students, leading to more discussion; an enjoyable way for students to collaborate on a university assignment and learn from each other. We plan on using the tool in future classes for discussing research papers in addition to scores.
Notes:
Click the HTML link to this paper to read it using a.nnotate.com - you can also attach comments / feedback to the author.
Abstract: Textensor Limited is developing tools for improving the communication and exploitation of text based information. Our main product, Notate, is a web based system that enables authors and readers to layer structured annotations on top of documents so that the resulting combination can be reliably processed automatically while maintaining the integrity of the original source and the provenance of all annotations.
The system has a wide variety of applications including attaching sticky notes and discussions to web pages, sharing documents and notes within a small group, on-line document review and sophisticated data curation tasks. It aims to bring the authoring of semantically rich structures within the capabilities of normal users, making it dramatically easier to produce well-structured content and opening up possibilities for further automated processes such as creating indexes to the research literature and curating more high-quality information into databases. The initial requirements and example applications are taken from the needs of the biomedical research community, but the core technology is not domain specific and has similar applications in other fields that deal with large volumes of documents containing complex and interlinked information.
This white paper describes the origin of the underlying ideas for Notate in hypertext and web research communities, and places our work in the context of other recent advances in web technologies such as semantic wikis and 'Web 2.0'.
Notes: See also http://a.nnotate.com for further developments.
