The language history questionnaire (LHQ) is an important tool for assessing the linguistic background of bilinguals or second language learners and for generating self-reported proficiency in multiple languages. Previously we developed a generic LHQ based on the most commonly asked questions in published studies (Li, Sepanski & Zhao, 2006). Here we report a new web-based interface (LHQ 2.0) that has more flexibility in functionality, more accuracy in data recording, and more privacy for users and data. LHQ 2.0 achieves flexibility, accuracy, and privacy by using dynamic web-design features for enhanced data collection. It allows investigators to dynamically construct individualized LHQs on the fly and allows participants to complete the LHQ online in multiple languages. Investigators can download and delete the LHQ results and update their user and experiment information on the web. Privacy issues are handled through the online assignment of a unique ID number for each study and password-protected access to data.

Transmission electron microscopy and associated methods, such as single particle analysis, two-dimensional crystallography, helical reconstruction, and tomography, are highly data-intensive experimental sciences, which also have substantial variability in experimental technique. Object-oriented databases present an attractive alternative to traditional relational databases for situations where the experiments themselves are continually evolving. We present EMEN2, an easy to use object-oriented database with a highly flexible infrastructure originally targeted for transmission electron microscopy and tomography, which has been extended to be adaptable for use in virtually any experimental science. It is a pure object-oriented database designed for easy adoption in diverse laboratory environments and does not require professional database administration. It includes a full featured, dynamic web interface in addition to APIs for programmatic access. EMEN2 installations currently support roughly 800 scientists worldwide with over 1/2 million experimental records and over 20 TB of experimental data. The software is freely available with complete source.

Learner corpora, electronic collections of spoken or written data from foreign language learners, offer unparalleled access to many hitherto uncovered aspects of learner language, particularly in their error-tagged format. This article aims to demonstrate the role that the learner corpus can play in CALL, particularly when used in conjunction with web-based interfaces which provide flexible access to error-tagged corpora that have been enhanced with simple NLP techniques such as POS-tagging or lemmatization and linked to a wide range of learner and task variables such as mother tongue background or activity type. This new resource is of interest to three main types of users: teachers wishing to prepare pedagogical materials that target learners' attested difficulties; learners themselves for editing or language awareness purposes and NLP researchers, for whom it serves as a benchmark for testing automatic error detection systems.