Evidence map›Paper›PMID 35816182›Full record

ArticleJournal of forensic sciences2022

Crowdsourcing forensics: Creating a curated catalog of digital forensic artifacts.

Eoghan Casey, Lam Nguyen, Jeffrey Mates, Scott Lalliss

Abstract read
In one paragraph

Article in Journal of forensic sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Eoghan CaseyDoD Cyber Crime Center (DC3), Linthicum Heights, Maryland, USA.ORCID https://orcid.org/0000-0001-7219-317X
Lam NguyenDoD Cyber Crime Center (DC3), Linthicum Heights, Maryland, USA.
Jeffrey MatesDoD Cyber Crime Center (DC3), Linthicum Heights, Maryland, USA.
Scott LallissDoD Cyber Crime Center (DC3), Linthicum Heights, Maryland, USA.

Funding

American Academy of Forensic Sciences
6 · The paper itself

Abstract

The increasing volume, variety, velocity, distribution, structural intricacy, and complexity of use of digital evidence can make it difficult for practitioners to find and understand the most forensically useful information (Casey E. Digital evidence and computer crime: Forensic science, computers, and the Internet. Academic Press; 2011. p. 31; Pollitt M. The hermeneutics of the hard drive: Using narratology, natural language processing, and knowledge management to improve the effectiveness of the digital forensic process [PhD dissertation]. University of Central Florida; 2011). Digital forensic practitioners currently search for information and solutions in an ad hoc manner, leading to results that are unstructured, unverified, and sometimes incomplete. As a result, certain digital evidence is being missed or misinterpreted. To mitigate risks of knowledge gaps, there is a pressing need for a systematic mechanism that practitioners can use to codify and combine their collective knowledge. This work presents the design and development of a solution that catalogs crowdsourced knowledge of digital forensic artifacts in a well-structured, easily searchable form to support efficient and automated extraction of pertinent information, improving availability and reliability of interpretation of artifacts (general acceptance). Technical implementation and artifact curation are discussed with illustrative examples and recommendations for future work.

Indexed as

ArtifactsCrowdsourcingForensic MedicineForensic SciencesReproducibility of Resultscrowdsourcing forensicsdigital forensic artifactdigital transformationforensic technology innovationgeneral acceptancetool testing automation

Identifiers

PMID35816182
PMCPMC9543441

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.