Towards lensfield: Data management, processing and semantic publication for vernacular e-science

Lensfield is a desktop and filesystem-based tool designed as a “personal data management assistant” for the scientist. It combines distributed version control (DVCS), software transaction memory (STM) and linked open data (LOD) publishing to create a novel data management, processing and publication tool. The application “just looks after” these technologies for the scientist, providing simple interfaces for typical uses. It is built with Clojure and includes macros which define steps in a common workflow. Functions and Java libraries provide facilities for automatic processing of data which is ultimately published as RDF in a web application. The progress of data processing is tracked by a fine-grained data structure that can be serialized to disk, with the potential to include manual steps and programmatic interrupts in largely automated processes through seamless resumption. Flexibility in operation and minimizing barriers to adoption are major design features.This paper was presented at the IEEE eScience conference 2009, hosted by the Oxford eResearch Centre and held at the Kassam Stadium outside Oxford

OSCAR4: a flexible architecture for chemical text-mining

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract The Open-Source Chemistry Analysis Routines (OSCAR) software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling) that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.Peer Reviewe

Machine-learned and codified synthesis parameters of oxide materials

Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials