The ASCLD Forensic Research Committee is proud to announce the launch of the Researcher / Practitioner Collaboration Directory. The goal is to create a directory to connect researchers with ongoing projects with forensic practitioners who are willing to participate in and provide support to forensic science research projects. It is ASCLD’s hope that these collaborations will result in comprehensive, high-impact research projects that will transition into the laboratory as new and enhanced forensic capabilities.

If you are a researcher and would like to submit your project for listing in the Directory, please click HERE.

Please use the following items to search the entries. You can use as many of the following as you need:

Section

Section

Section

Section

Section


Forensic geology and soil evidence: Current status and needs in Forensic Community

Research Organization:  Arizona State University
Principal Investigator:  Gwyneth Gordon

Funding Source:  none
Other Collaborators Involved:  none
Email Address:  gwyneth.gordon@nullasu.edu
Phone Number:  4809936600
Website/URL:  https://forms.gle/N6c2qdBWxPPgh2pd7
Discipline:  Trace Chemistry
Instrumentation Involved:  none

Abstract: 

There has been no published survey of law enforcement and forensic practitioners to determine how frequently soil and geolgoical evidence is collected and used. AIM 1) Determine the current frequency of soil collection and forensic analysis AIM 2) Determine what the unmet needs of the forensic and law enforcement communities are for soil and geological evidence. The de-identified information collected will be relayed to the Organization of Scientific Area Committee of the National Institute of Standards and Technology. The PI is a member of the Geological Materials Task Group within the Trace Evidence section of OSAC (https://www.nist.gov/topics/organization-scientific-area-committees-forensic-science/geological-materials-subcommittee), although they are not funding nor are they responsible for this study. This study is approved by the Arizona State University Institutional Research Board as IRB study #00016934.If you have any questions about your rights as a subject/participant in this research, or if you feel you have been placed at risk, you can contact the Chair of the Human Subjects Institutional Review Board, through the ASU Office of Research Integrity and Assurance, at (480) 965-6788.

Study Dates:  December 1, 2022 – May 30, 2023
Support Requested:  distribution and response to survey
Estimated Participant Time Involved:  15 minutes
Deliverable Anticipated:  Peer-reviewed article, Oral Presentation, Poster Presentation


Assessment of Forensic Footwear Impression Quality by Human Raters

Research Organization:  West Virginia University
Principal Investigator:  Dr. Jacqueline Speir (with co-investigator Ms. Lily Lin)

Funding Source:  N/A
Other Collaborators Involved: 
Email Address:  el0049@nullmix.wvu.edu
Phone Number: 
Website/URL:  https://wvu-speir-research-group.shinyapps.io/shiny_shoe/
Discipline:  Footwear
Instrumentation Involved:  Computer

Abstract: 

Shoeprints deposited during the commission of a crime vary in quality as a function of numerous factors, including substrates, media, and the physical activities carried out by perpetrators. This variability impacts the value and quantity of information available in questioned crime scene impressions, and therefore the strength of an examiner’s opinion concerning source attribution. To date, limited research has been conducted to quantify footwear image quality. In response, this study aims to develop a footwear quality assessment model to define footwear impression quality using human assessments as a guide, combined with image factors such as totality, noise, contrast, etc. To collect human ratings, participants will review 55 pairs of impressions via a web browser and using an R-shiny application interface. For each pair of impressions, participants will evaluate the type and reliability of information available in the impressions, as well as the highest level of source-attribution that can be reached based on the totality of information present. Experts’ responses will be used to calculate intra- and inter-rater-reliability, and median/mean/predicted ratings will be used to inform an ordinal logistic regression model designed to predict image quality based on impression features. This study has an IRB (Institutional Review Board) approved protocol on file with WVU (West Virginia University) and can be accessed via the following URL: https://wvu-speir-research-group.shinyapps.io/shiny_shoe/

Study Dates:  November 1, 2022 – May 31, 2023
Support Requested:  Willingness to review and rate images of footwear impressions.
Estimated Participant Time Involved:  1 to 1.5 hours
Deliverable Anticipated:  Peer-reviewed article


Solving the DNA Mixture Conundrum with Single-Cell analysis

Research Organization:  Rutgers University Camden
Principal Investigator:  Catherine Grgicak

Funding Source:  National Institute of Justice
Other Collaborators Involved:  Desmond S. Lun; Ken R. Duffy
Email Address:  c.grgicak@nullrutgers.edu
Phone Number:  617-913-9728
Website/URL:  http://www.lftdi.com
Discipline:  Biology/Serology
Instrumentation Involved:  Bespoke Software

Abstract: 

We are interested in collaborating with a crime laboratory partner interested in exploring single-cell analysis for forensic purposes. We have a bespoke algorithm capable of interpreting single-cell EPGs from diploid cells across multiple clusters and are now working on expanding the model to be able to address haploid results. This work is of forensic relevance since the single cell likelihood ratio (LR) simplifies to it most informative form — i.e., the number of contributors being one and only one — which means that multiple contributors would not have jointly contributed to the group of cells. Recently, we developed an extension to the bespoke algorithm that allows us to report the weight of evidence (WoE) across all clusters of an admixture of cells, and these WoE were ca. [25-30) regardless of the TrueNOCs. We seek laboratories willing to alpha-test the software using their own samples, or samples we can provide.

Study Dates:  January 1, 2023 – December 31, 2024
Support Requested: 
Estimated Participant Time Involved:  2 hours per month
Deliverable Anticipated:  Peer-reviewed article, Oral Presentation


Triaging Crime Scene Items

Research Organization:  Yale University and University College London
Principal Investigator:  Ifat Levy

Funding Source:  National Institutes of Health (NIH) and Yale University
Other Collaborators Involved:  Ruth Morgan, Mohammed Almazrouei
Email Address:  ifat.levy@nullyale.edu
Phone Number:  (203) 737-1374
Website/URL: 
Discipline:  Crime Scene Investigation
Instrumentation Involved:  Online Study

Abstract: 

Would you like to participate in a study about factors affecting forensic decision making?

Dear Participant,

Judgments made at or after crime scene investigations can be crucial for legal proceedings. The aim of this study is to explore factors that may play a role in the decision-making of triaging items collected from crime scenes.

We are seeking volunteers to participate in this online study. The inclusion criteria are as follows: an adult crime scene/forensic examiner or supervisor/manager who:

1.Can be involved in the process of prioritizing or triaging items collected from crime scenes; and
2.Can be involved in the selection of testing type for triaged items, including testing for biological traces (like blood) and fingerprints.

The participant can be working in any relevant sections/ departments (such as crime scene, evidence recovery, or biology) as long as they can be involved in both of the above forensic tasks.

Fluency in English is necessary for the completion of the study tasks.

As a participant, you will be asked to evaluate a forensic casework brief and photographs of items collected from a crime scene. Under a hypothetical scenario in which you are the expert assigned to this case, you will make a series of decisions about the case.

You will also be asked to complete a task in which you will make a series of choices between pairs of visual options. You will be asked to choose which of the two options you prefer. For example, you might be asked to choose whether you would prefer an option with a 100% chance of a certain outcome, or an option with another probability of a different outcome.

The study will take approximately 30-40 minutes to complete. There is time for a short break in the middle if you need it.

Participation is voluntary and you are free to quit at any time. Although you will be asked some basic demographic questions (e.g., age), we do not collect any individual identifying information. Thus, all data collected for this study are anonymous.

If you are interested in participating in this study, please:

-Use this link: https://uclresearch.fra1.qualtrics.com/jfe/form/SV_ex66RdIdyb6w8gm
-Make sure that your audio is turned on. Some tasks will require you to hear recordings.
-Take part in this study only if you are using a desktop/ laptop computer.

This study was approved by Yale HIC (#0910005795). If you have any questions about this study, please contact the Principal Investigator, Dr. Ifat Levy, at ifat.levy@nullyale.edu

Thank you very much.

Yale Decision Neuroscience Lab.

Study Dates:  March 29, 2022 – December 31, 2022
Support Requested:  Participation from practitioners in completing the online study is appreciated. For this study, the participant will be asked whether they would choose to test (or not) crime scene items for two broad types of forensic analyses: biological traces (like blood) and fingerprints. The targeted participants are any crime scene/forensic examiner or supervisor/manager who could be ‘involved’ in the triaging process. The participant can be working in any relevant sections/ departments (such as crime scene, evidence recovery, or biology, etc) if they can be involved in the item prioritization process at any stage (e.g., in the crime scene, in the laboratory, in a managerial meeting). Fluency in English is necessary for the completion of the study tasks.
Estimated Participant Time Involved:  About 30-40 minutes; on one occasion only
Deliverable Anticipated:  Peer-reviewed article, Oral Presentation


Blind Proficiency Testing

Research Organization:  Carnegie Mellon, University of Virginia
Principal Investigator:  Robin Mejia

Funding Source:  National Institute of Standards and Technology
Other Collaborators Involved:  Sharon Kelley, Brett Gardner
Email Address:  rmejia@nullandrew.cmu.edu
Phone Number: 
Website/URL:  https://forensicstats.org/latent-print-analysis/
Discipline:  Latent Prints
Instrumentation Involved: 

Abstract: 

In this project, we will work with laboratories to implement or increase the use of blind proficiency testing. We will document these experiences in CSAFE white papers covering lessons learned and best practices for laboratories of a range of sizes. We will evaluate the results of these programs to provide data of use to laboratories themselves and to better assess LPA as performed in practice, and we will work with laboratories to develop a collaborative infrastructure to support the implementation of blinding in mid-sized and possibly smaller laboratories that do not have the infrastructure of larger laboratories (e.g., HFSC). Our overarching goal is to work with labs to develop and implement their own internal processes for blind proficiency testing.

Study Dates:  March 1, 2022 – May 31, 2024
Support Requested:  CSAFE researchers have collaborated with laboratories to analyze data from existing blind proficiency testing programs and have consulted with laboratories that are considering or are in the process of establishing blind proficiency testing programs. We welcome contacts from laboratories that conduct blind PT or are considering it. Collaborations can range from a single discussion to ongoing logistical or analytical support, potentially including connecting those who would be interested in inter-laboratory collaboration.
Estimated Participant Time Involved:  Varies depending on level of interest.
Deliverable Anticipated:  Peer-reviewed article, SOPs for blind proficiency testing


Mobile App Evidence Analysis

Research Organization:  Iowa State University
Principal Investigator:  Dr. Yong Guan

Funding Source:  National Institute of Standards and Technology
Other Collaborators Involved: 
Email Address:  guan@nulliastate.edu
Phone Number: 
Website/URL:  https://forensicstats.org/digital-evidence/
Discipline:  Digital Evidence
Instrumentation Involved: 

Abstract: 

Researchers focus on designing and prototyping EviHunter, a suite of automated Android app analysis tools to analyze and discover Android-app-generated evidential data (i.e., forensic artifacts), stored in local storage, SQLite database, and/or at a remote third-party server. With EviHunter, researchers are working with NIST to build a catalog-like Android app forensic artifact database, which will host the forensic evidential artifacts (e.g., type, location, and data format) that mobile apps generated and stored on the mobile devices or remote servers. We are creating a public web portal to allow digital evidence investigators to query the database and obtain the list of evidential artifacts for the app of interest in a casework.

Researchers are also developing virtual machine-based EviHunter VM images. With the support of EviHunter team, practitioners can utilize their own computers or third-party cloud services, for example Amazon AWS or Google Cloud, to run their own instances of EviHunter to analyze the apps in their caseworks. In addition, the Android apps (latest version, as well as its older versions) analyzed by EviHunter will be hosted in a separate app database, which is only accessible to the EviHunter team. In this project, we will also investigate software optimization and obfuscation techniques that change the resulted apps’ APKs, and conduct third-party SDK analysis. A large-scale evaluation using real-world Android apps and the development of Android app forensic artifacts database are on-going.

Study Dates:  March 1, 2022 – May 31, 2024
Support Requested:  We will work with the crime lab practitioners to get a better understanding on the frequently-used digital evidence types in casework, areas of confusion and potential errors in communicating and analyzing these evidence types. We hope to gain insights on what factors limit the practitioners’ efficacy and efficiency in their daily casework.
Estimated Participant Time Involved:  Varied, depending on actual work and the need for further clarifications. Usually, one or two hours every two months. We plan to visit the crime labs for in-person and/or virtual Zoom meetings, based on the needs and mutual interest/availability.
Deliverable Anticipated:  Peer-reviewed article, EviHunter app


StegoAppDB: a reference database with variational sources for mobile steganography image forensics

Research Organization:  Iowa State University
Principal Investigator:  Dr. Jennifer Newman

Funding Source:  National Institute of Standards and Technology
Other Collaborators Involved:  Roy Maxion (Carnegie Mellon)
Email Address:  jlnewman@nulliastate.edu
Phone Number: 
Website/URL:  https://forensicstats.org/digital-evidence/
Discipline:  Digital Evidence
Instrumentation Involved: 

Abstract: 

The CSAFE team at ISU has developed and made public a reference dataset, StegoAppDB, comprised of more than 960,000 highly-provenanced image data created using our unique approach of modeling the steganography (stego) software applications (because their algorithms are not public). The data is used to develop and test algorithms and create new models of the camera pipeline. The next generation of algorithms for detecting hidden images will rely on deep learning algorithms with millions of free parameters (see, e.g., Boroumand et al., 2019). We are growing the database substantially through automation of the image data collection and stego creation process, such as using a drone to collect copious amounts of image data — in order to allow investigators to work with deep learning methods. We propose to add image data selectively to enhance the attractiveness of StegoAppDB for wider use in the forensic imaging community, in the following manner:

1. Add several million outdoor images so that the most successful machine learning techniques, deep learning neural networks, have access to ample data.
2. Add images that have been photo-edited before embedding payload, which we suspect is a common occurrence. In fact, it is speculated that image editing operations may affect steganalysis error rates, but little to nothing is known about this, and certainly nothing for mobile stego images.
3. Include stego apps beyond those currently in StegoAppDB and create an in-house detection tool for verification of our stego process, since no software package exists specifically to detect stego images created with mobile stego apps. We will use emulators and reverse engineering for the stego app analysis to retain accurate parameters for data provenance.

Study Dates:  March 1, 2022 – May 31, 2024
Support Requested:  Availability to discuss the issues with steganography/digital image forensics the crime lab currently has or is looking to address in the future; if a topic of mutual interest is identified, then some sample of data plus a description of the issues encountered.
Estimated Participant Time Involved:  The time is TBD. We would work with the lab to identify some aspect of the issue that could be studied based on the availability of the lab personnel.
Deliverable Anticipated:  Peer-reviewed article, Database of stego images and analysis tools


Forensic Processing at Crime Labs

Research Organization:  University of Virginia
Principal Investigator:  Dr. Brett Gardner

Funding Source:  National Institute of Standards and Technology
Other Collaborators Involved:  Robin Mejia (Carnegie Mellon), Dan Murrie (UVA), Sharon Kelley (UVA)
Email Address:  bg2dd@nullvirginia.edu
Phone Number: 
Website/URL:  https://forensicstats.org/latent-print-analysis/
Discipline:  Latent Prints
Instrumentation Involved: 

Abstract: 

A primary goal of this project is to provide field data regarding the practice of latent print comparison in crime laboratories. We intend to collaborate with laboratory personnel to examine case management procedures and related data in multiple laboratories. This will expand the limited research of real-world outcomes and allow for inter-laboratory comparison. We will study actual casework and laboratory procedures with the goal of offering recommendations for practices that promote increased accuracy, reliability, and/or efficiency.

Study Dates:  April 1, 2022 – May 31, 2024
Support Requested:  CSAFE researchers have successfully collaborated with crime laboratories to examine case processing variables and clarify work flow procedures in latent print comparison units in recent years (e.g., Rairden et al., 2018; Gardner et al., 2021). Researchers are seeking to collaborate with laboratory personnel to generate and answer questions about each laboratory’s case processing and workflow. Collaboration will aim to answer the laboratories’ specific questions and inform the larger field of case processing within latent print units.
Estimated Participant Time Involved:  Time for the collaboration can vary according to a lab’s specific aims. For laboratories with available case processing data, a roughly one hour consultation with researchers to develop/finalize research questions and explain existing datasets might be all that’s needed at the outset. Laboratories with the desire to begin collecting case processing data can collaborate with researchers as needed to facilitate the development of research questions, data collection, analysis, and interpretation of results.
Deliverable Anticipated:  Peer-reviewed article


Handwriting Evaluation

Research Organization:  Iowa State University
Principal Investigator:  Dr. Alicia Carriquiry

Funding Source:  National Institute of Standards and Technology
Other Collaborators Involved:  Danica Ommen (Iowa State), John Libert (NIST)
Email Address:  alicia@nulliastate.edu
Phone Number: 
Website/URL:  https://forensicstats.org/handwriting-analysis/
Discipline:  Handwriting
Instrumentation Involved: 

Abstract: 

The overall goal of this project is to develop tools to aid handwriting and document examiners in their evaluations. The approach we propose treats handwriting as a collection of graphical objects, from which we can extract features. These features are hypothesized to be informative about the writer of the document, so that they can be used to compute a probability of authorship within a closed set of potential authors of the questioned document. (Issues such as motive, access and opportunity also impact the overall probability of writership, but are beyond the scope of this project.) An ongoing challenge is to extend the approach to the situation where the writer of a document is not a member of a closed set.

Study Dates:  April 1, 2022 – May 31, 2023
Support Requested:  Participants will be asked to provide handwriting samples at three data collection sessions, each at least three weeks apart. At each session, participants complete a short survey and transcribe the contents of three prompts, each three times. In an upcoming project phase, we will ask participants to test the handwriter package on their own samples and provide feedback on the user interface, capabilities, limitations, usefulness, and other usability features.
Estimated Participant Time Involved:  3-4 hours total over a period of 2-3 months
Deliverable Anticipated:  Peer-reviewed article, Database of handwriting samples, algorithms


Characterization of Footwear in Local Populations

Research Organization:  University of Nebraska, Lincoln
Principal Investigator:  Dr. Susan Vanderplas

Funding Source:  National Institute of Justice, National Institute of Standards and Technology
Other Collaborators Involved:  Dr. Richard Stone (Iowa State), Steve Lund (NIST), Marty Herman (NIST)
Email Address:  susan.vanderplas@nullunl.edu
Phone Number: 
Website/URL:  https://forensicstats.org/footwear/
Discipline:  Footwear
Instrumentation Involved:  Footwear scanner developed as part of NIJ funded effort

Abstract: 

This project will develop scanning equipment which will be used to capture population footwear class characteristics. In concert, we will develop statistical software which will automatically identify class characteristics and other comparison features. We will collect data from several different populations and compare the prevalence of class characteristics in those populations, using these results to develop guidelines for future data collection projects.

Study Dates:  June 1, 2022 – May 31, 2024
Support Requested:  We are looking for practitioners and law enforcement partners to collect data from local populations
Estimated Participant Time Involved:  Ongoing and dependent on agency
Deliverable Anticipated:  Peer-reviewed article


DNAmix 2021

Research Organization:  Noblis and Bode Technology
Principal Investigator:  Austin Hicklin and Jon Davoren

Funding Source:  NIJ
Other Collaborators Involved: 
Email Address:  hicklin@nullnoblis.org
Phone Number:  703-217-4668
Website/URL:  https://dnamix.edgeaws.noblis.org
Discipline:  Biology/Serology
Instrumentation Involved: 

Abstract: 

Please participate in DNAmix 2021 — this large-scale independent study is being conducted to evaluate the extent of consistency and variation among forensic laboratories in interpretations and statistical analyses of DNA mixtures, and to assess the effects of various potential sources of variability. The study is being conducted by Noblis and Bode Technology, under a grant from NIJ. Register at https://dnamix.edgeaws.noblis.org

Study Dates:  July 6, 2021 – January 31, 2022
Support Requested:  Participation
Estimated Participant Time Involved:  12 hours
Deliverable Anticipated:  Peer-reviewed article, Oral Presentation


Case Contextual Information and its Variation among Forensic Laboratories

Research Organization:  Duquesne University Forensic Science and Law Program
Principal Investigator:  Taylor Hopkins

Funding Source:  Duquesne University
Other Collaborators Involved:  Lyndsie Ferrara, Ph.D.
Email Address:  hopkinst1@nullduq.edu
Phone Number: 
Website/URL:  https://duq.az1.qualtrics.com/jfe/form/SV_9ZegMKAFFn32gnQ
Discipline:  All disciplines may participate!
Instrumentation Involved: 

Abstract: 

Contextual bias is one of the most common biases discussed in the forensic science community as it can cause scientists to let case contextual information guide their decisions as opposed to the actual evidence. The purpose of my research is to investigate the amount of case contextual information requested by forensic laboratories, via their lab submission forms, and how they vary between forensic disciplines. Data will be collected using a survey that asks participants about their laboratory, laboratory submission form, and laboratory procedures as it relates to case contextual information and how it is transmitted. While more research is being done, current research articles evaluate the procedures within specific disciplines without giving a holistic view of how the forensic science community is combatting bias. The data collected will provide a comprehensive outlook on what different laboratories and disciplines are doing, or not doing, to mitigate the effects of contextual bias.

Study Dates:  June 21, 2021 – August 31, 2021
Support Requested:  Participants of this study will be asked to fill out a survey describing their laboratory, laboratory submission form, and laboratory procedures as it relates to case contextual information and how it is transmitted. Participation is voluntary and the participant may discontinue at any time. The form should take 5-10 minutes to complete. The survey is also anonymous and has been reviewed and approved by the IRB at Duquesne University.
Estimated Participant Time Involved:  5-10 Minutes
Deliverable Anticipated:  Oral Presentation, Poster Presentation


Calculation of likelihood ratios from forensic comparison of fired cartridge cases

Research Organization:  Staffordshire University & Aston University
Principal Investigator:  Dr Rachel Bolton-King & Dr Geoffrey Morrison

Funding Source:  Research England, Expanding Excellence in England (E3)
Other Collaborators Involved:  Dr Basu & Dr Vogiatzis, Aston University
Email Address:  r.bolton-king@nullstaffs.ac.uk
Phone Number: 
Website/URL: 
Discipline:  Firearms/Toolmarks
Instrumentation Involved:  Evofinder Data Acquisition System

Abstract: 

Dr Bolton-King is creating a database of scans of 9 mm Luger type cartridge cases fired from semi-automatic pistols. The aim is to scan 10 cartridge cases fired from each of 1000 pistols (10,000 cartridge cases total). We currently only have 30% of our required number and therefore urgently seek support from practitioners in creating test fires for our database.

Ammunition used in test firing should have a brass primer cap and brass cartridge case. The pistols we seek test fires from should have parallel breechface marks and hemispherical firing pin impressions. Example 9mm Luger firearms include Sig Sauer P226 and P228, Hi-Point C9, Beretta 92, Ruger P85, P89 and P94, Smith & Wesson 59, Springfield P9, Browning Hi-Power, CZ 75, Kahr K9, Kel-Tec P-11, and their variants. This list is not comprehensive so please contact Rachel directly for further details if you are able to assist.

Dr Morrison and his team will subsequently exploit the database to develop and validate a system that uses image-processing and machine-learning techniques to calculate likelihood ratios addressing the hypotheses that questioned- and known-origin cartridge casings were fired from the same pistol versus that they were fired from different pistols (pistols that fire 9 mm Luger type ammunition).

Study Dates:  April 25, 2021 – July 23, 2021
Support Requested:  10 fired cartridge cases from as many 9mm Luger firearms with parallel breechface marks and hemispherical firing pin impressions as possible. Submission of the corresponding test-fired bullets would also be of value, but are not essential for this phase of the research.
Estimated Participant Time Involved:  Unknown – it may depend on the number of available and relevant firearms in your collection
Deliverable Anticipated:  Peer-reviewed article, Oral Presentation