Forensic Data Hackathon #DataFest2019

Leverhulme Research Centre for Forensic Science, University of Dundee

Join the LRCFS Forensic Data Hackathon at #DataFest2019

Ewing Building

For this hackathon we want you to come up with new, interesting and different, interpretations of two important forensic science data sets that we have created AND at the end there will be pizza!High quality forensic science is underpinned by reliable data. We need it, for example, in order to be able to calculate the likelihood of someone's DNA profile matching a crime scene sample rather than the DNA profile of a random person. The two datasets we would like you to look at are:•	Ballistic analysis of high velocity air pistol pellets•	Chemical analysis of ignitable liquids from multiple different sourcesThe data sets will open on the Monday 11 March and the hackathon will end with a presentation of the results at the Leverhulme Research Centre for Forensic Science on Friday 22 March. Members of the LRCFS team will be there to answer questions you have. We welcome people to attend who would like to see the outcomes of the hackathon too.The aims of the LRCFS is to positively disrupt the Forensic Science community to improve how forensic evidence is processed, analysed, interpreted and ultimately presented in court. We will do this whilst being as open and transparent as possible, making all our data, methods and research fully open by default. One aspect is to create what we call "Ground Truth" datasets where the data were collected under controlled conditions which can then be used to make reliable inferences about potential event scenarios. The ground truth data can then be compared to casework data and determine whether courtroom explanations are consistent with the data or not.Numbers are strictly limited so please make sure to register. Everyone who intends to participate either in the two-week hackathon itself or on the presentation day needs to register.TasksFor either one or both of the datasets choose one or more of the following tasks:1.	Prepare the data such that it can be read programmatically, whether that is via flat files like CSV or structured files like JSON is up to you.2.	Develop a model which can classify the sources given only the data.2.1. Present the data classes in a visually impactful or interesting way3.	Create a predictor which can make predictions of the source material (for the liquids) or the firing conditions (for the pellets) from sample input only and quantify their reliability/accuracy. The DatasetsAir pistol pellets: A dataset of approximately 600 shots fired by the same air pistol one after the other. The pellets were then examined under an infinite focus microscope which accounted for the curvature of the image and photographed for 'lands' and 'grooves' indicative of the rifling present in the pistol's barrel. The dataset comprises the processed data from the images and the images themselves describing the size and shape of the lands and grooves detected on each pellet and the metadata associated with how the pistol was fired into what material.The open questions are: can the land and groove information be used to differentiate the air pistol from an air rifle? Can compressed pellets with partial information be linked to the pellets from the pistol? As the firings were all sequential, can the data identify the order in which a random subset of pellets were fired?Ignitable liquids: Three samples of four different liquid types (petrol, diesel, lighter fluid, MPD) from up to 14 different suppliers were obtained and their chemical compositions analysed with gas chromatography-mass spectrometry (GC-MS). The raw data are a series of peaks (corresponding to separate compounds) with their retention times and peak areas. The peak areas are proportional to the amount of each of the compounds in the liquids. They need to be standardised to an internal standard for comparison between runs. The retention times are approximately the same for a given compound in different runs, but not identical. The peak areas are proportional to the amount.The neat liquids have also been submitted to a serial dilution experiment by 10%, 25%, 50%, 75%, 90% and 95% of the original.The open questions are: can the individual liquid types and sources be separated when undiluted and then when diluted? For example, if given a 90% diluted unknown sample how reliably can its source and type be identified?The Leverhulme Research Centre for Forensic Science is a £10 million research centre funded for 10 years by the Leverhulme Trust. The Centre has positive disruption at its very core, creating an interdisciplinary space where our researchers interact on a regular basis with people from across the criminal Justice Space. Our collaborators are global and are drawn from the senior Judiciary, law enforcement, forensic practitioners, crime scene investigators, innovators and entrepreneurs. We facilitate honest conversations articulating co shared challenges and delivering collaborative solutions to support a fair and just criminal justice system.The data recorded in this form will be anonymised and shared with The Data Lab for reporting purposes and to help measure the impact of DataFest 2019.Please be aware that photography and filming will take place during the event. The photos and video will be used to promote future events on the LRCFS, University of Dundee, DataFest, and/or Data Lab websites and social media channels and on digital and/or print promotional materials.If you do not want to take part, please let us know at registration on the day, or contact us at <a href="http://mailto:lrc@dundee.ac.uk" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">LRC@DUNDEE.AC.UK</a> and we will give you an identifier so the photographers/videographers know not to include you.

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Forensic Data Hackathon #DataFest2019

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Forensic Data Hackathon #DataFest2019

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