Dataset of coded handwriting features for use in statistical modelling.

The data presented here is related to the article titled, "Using handwriting to infer a writer's country of origin for forensic intelligence purposes" (Agius et al., 2017) [1]. This article reports original writer, spatial and construction characteristic data for thirty-seven English Australian writers and thirty-seven Vietnamese writers. All of these characteristics were coded and recorded in Microsoft Excel 2013 (version 15.31). The construction characteristics coded were only extracted from seven characters, which were: 'g', 'h', 'th', 'M', '0', '7' and '9'. The coded format of the writer, spatial and construction characteristics is made available in this Data in Brief in order to allow others to perform statistical analyses and modelling to investigate whether there is a relationship between the handwriting features and the nationality of the writer, and whether the two nationalities can be differentiated. Furthermore, to employ mathematical techniques that are capable of characterising the extracted features from each participant.

Using handwriting to infer a writer's country of origin for forensic intelligence purposes.

Forensic science has traditionally focused the majority of its resources and objectives towards addressing Court-related questions. However, this view restricts the contribution of forensic science to one process and results in a loss of information as the investigative and intelligence roles are largely neglected. A forensic science discipline suffering from this imbalance is handwriting examination, which may be characterised as a time consuming and subjective process that is mostly carried out towards the end of the investigation for the purpose of judicial proceedings. Individual and habitual characteristics are the major handwriting features exploited, however alternate information concerning the author's native language could potentially be used as a key element in an intelligence framework. This research focussed on the detection of characteristics that differentiate Vietnamese and English Australian writers based on their English handwriting. The study began with the extraction of handwriting characteristics from the writing of people from the two populations. The data was analysed using a logistic regression model and a classification and regression tree (CRT). Each recognised four class characteristics that were capable of distinguishing between the two nationalities. The logistic regression and CRT models were both capable of correctly predicting 93% of cases. Their predictive capabilities were then tested and supported using blind exemplars in order to mirror casework settings. It appeared that when using their respective class characteristics, the two models were capable of differentiating English Australians from Vietnamese in the data set. This proof of concept research demonstrated the plausibility of exploiting this additional information from a handwriting trace and taking advantage of it in an intelligence-led framework.

The use of handwriting examinations beyond the traditional court purpose.

Traditionally, forensic science has predominantly focused its resources and objectives on addressing court related questions. However, this view restricts the contribution of forensic science to one function and results in lost opportunities as investigative and intelligence roles are often overlooked. A change of perspective and expansion of the contributions of forensic science is required to take advantage of the benefits of abductive and inductive thought processes throughout the investigative and intelligence functions. One forensic discipline that has the potential to broaden its traditional focus is handwriting examination. Typically used in investigations that are focused on both criminal and civil cases, the examination procedure and outcome are time consuming and subjective, requiring a detailed study of the features of the handwriting in question. Traditionally, the major handwriting features exploited are characteristics that are often considered individual (or at least highly polymorphic) and habitual. However, handwriting can be considered as an information vector in an intelligence framework. One such example is the recognition of key elements related to the author's native language. This paper discusses the traditional method generally used around the world and proposes a theoretical approach to expand the application of handwriting examination towards gaining additional information for intelligence purposes. This concept will be designed and tested in a future research project.

Analysis of amphetamine-type substances by capillary zone electrophoresis using capacitively coupled contactless conductivity detection.

CE with capacitively coupled contactless conductivity detection (C(4)D) was employed for the separation and detection of seven amphetamine analogues as well as amphetamine, dextroamphetamine, methamphetamine and 3,4-methylenedioxymethamphetamine. The separation electrolyte was 30 mM hydroxypropyl-beta-cyclodextrin (HPbetaCD) in a 75 mM acetic acid+25 mM sodium acetate buffer adjusted to pH 4.55. Conductivity detection was compared with UV detection using this same electrolyte. Average detection limits for C(4)D and UV were 1.3 and 1.0 ppm, respectively. The effects of HPbetaCD concentration and BGE composition on the selectivity of the separation were also investigated. An illicit, street-grade sample of 3,4-methylenedioxymethamphetamine (Ecstasy) and a prescription dextroamphetamine tablet were also analysed.