Software & Algorithms Catalog

Software & Algorithms Catalog

The Software & Algorithms Catalog serves as a searchable database of forensically relevant algorithms and software across various forensic disciplines. The catalog is a tool for guiding future research in this important topic area and will provide a deeper understanding of the state of practice in algorithmic forensic science, including identification of areas without algorithms and software.

Note: Information is provided by practicioners within disciplines. Any mention of commercial or non-commercial products is for information only and does not imply that a product has been tested.

The website is divided into 2 major sections:

  1. A search feature to find software & algorithms.
  2. A software and algorithms taxonomy with description of each discipline and the software/algorithms user base category list.

How to search:

Select forensic discipline category from menu, select find all to review all submissions for specified discipline or refine by user base category (open source, commercially available, law enforcement, restricted, research software).

Note: Selection of search by user base category will not show any submissions that do not have a specified category.

Background:

Computational science is a new frontier in improving forensic science by opening up new areas that can be understood using the power of computers. In the data rich world we live in, computational science is often the only way to process and understand a diverse set of artifacts that are available for analysis in criminal cases. Computational forensic science is built on algorithms and the software systems that execute those algorithms.

GAO Report Technology Assessment: Algorithms Used in Federal Law Enforcement

H.R.4368 - Justice in Forensic Algorithms Act of 2019

Submissions for relevant software and algorithms to be added to the catalog may be sent to Corrine Lloyd (corrine.lloyd@nist.gov) along with any questions.

This page was last modified on January 28, 2022 at 09:21.