Data for validation of osteometric methods in forensic anthropology
Natalie R. Langley, Lee Meadows Jantz, Shauna McNulty and 3 more
Data in Brief · 2018
doi:10.1016/j.dib.2018.04.148 (opens in a new tab)
We email you when a new notice or a question about this paper appears.
Abstract
Many techniques in forensic anthropology employ osteometric data, although little work has been done to investigate the intrinsic error in these measurements. These data were collected to quantify the reliability of osteometric data used in forensic anthropology research and case analyses. Osteometric data (n = 99 measurements) were collected on a random sample of William M. Bass Donated Collection skeletons (n = 50 skeletons). Four observers measured the left elements of 50 skeletons. After the complete dataset of 99 measurements was collected on each of the 50 skeletons, each observer repeated the process for a total of four rounds. The raw data is available on Mendeley Data (DCP Osteometric Data, Version 1. DOI: 10.17632/6xwhzs2w38.1). An example of the data analyses performed to evaluate and quantify observer error is provided for the variable GOL (maximum cranial length); these analyses were performed on each of the 99 measurements. Two-way mixed ANOVAs and repeated measures ANOVAs with pairwise comparisons were run to examine intraobserver and interobserver error, and relative and absolute technical error of measurement (TEM) was calculated to quantify the observer variation. This data analysis supported the dissemination of a free laboratory manual of revised osteometric definitions (Data Collection Procedures 2.0 [1], pdf available at https://fac.utk.edu/wp-content/uploads/2016/03/DCP20_webversion.pdf) and an accompanying instructional video (https://www.youtube.com/watch?v=BtkLFl3vim4). This manual is versioned and updatable as new information becomes available. Similar validations of scientific data used in forensic methods would support the ongoing effort to establish valid and reliable methods and protocols for proficiency testing, training, and certification.
Abstract licence: CC BY-NC
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.