OSAC	research	need	assessment	 v	1.0 1	

OSAC	Research	Need	Assessment	

Title	of	research	need: Date	posted:	

Keywords:	

Submitting	subcommittee(s):	

(If	SAC	review	identifies	additional	subcommittees,	add	them	to	the	box	above.)	

Background	information:

1. Description	of	research	need:

2. Key	bibliographic	references	relating	to	this	research	need:

3a.		In what	ways	would	the	research	results	improve	current	laboratory	capabilities?	

3b.		In what	ways	would	the	research	results	improve	understanding	of	the	scientific	basis	for	the	

subcommittee(s)?	

Databases, statistics, computation, forensic science, interpretation, trace evidence, transfer.

Jan.29.16

Trace/ Materials subcommittee

See attached file.

Development of an Integrated and Multidisciplinary Approach for the Advancement of Data Collection, Data Management and Data Analysis

 to Aid Interpretation of Trace Evidence. 

The proposed research would help close gaps in knowledge/research that are fundamental to the

 advancement of the interpretation of trace evidence. The development of reliable, representative, 

up-to-date databases would assist current laboratories with evaluating trace evidence and providing 

conclusions, including strengths and limitations, that are firmly supported by science.

See attached file.

The proposed research would provide the scientific basis for a better assessment of the value of trace

 materials in forensic, investigative and intelligence operations. The overall approach would provide

 essential information on validation studies, error rates, population studies, information systems, 

databases, data mining and data management that can be used to support the validity of scientific 

conclusions.

see above
OSAC	research	need	assessment	 v	1.0 2	

3c.		In	what	ways	would	the	research	results	improve	services	to	the	criminal	justice	system?	

4. Priority	assessment	(I,	II,	III,	or	IV):

This	research	need	has	been	identified	by	one	or	more	subcommittees	of	OSAC	and	is	being	provided	as	an	

informational	resource	to	the	community.

Major gap	

in	current	

knowledge

Minor gap	

in	current	

knowledge

No current	

research	is	being	

conducted

I	 III	

Existing current	

research	is	being	

conducted

II IV	

//	 	 	 Approval	date:	

(Approval	is	by	majority	vote	of	subcommittee.		Once	approved,	forward	to	SAC.)

1. Does	the	SAC	agree	with	the	research	need?	 Yes							⃝							No							⃝			

2. Does	the	SAC	agree	with	the	priority	assessment? Yes								⃝							No							⃝		

If	no,	what	is	the	priority	assessment	of	the	SAC:

Approval	date:	

(Approval	is	by	majority	vote	of	SAC.		Once	approved,	forward	to	QIC	for	posting	to	KAVI.)	

Date	posted	to	KAVI:			

Subcommittee

SAC

QIC

I

The research would be fundamental in moving trace evidence analysis field towards a more efficient approach

for assessing the value of evidence within the criminal justice system. The proposed research is anticipated

to add informative value and validity to scientific examinations and interpretations, and provide consistency

within the scientific community with regards to the interpretations and conclusions presented to the courts.

01/28/16 (18 yes; 2 absent)
3

OSAC	Research	Need	Assessment - Appendix

Title	 of	 research	 need:	 	 Development	 of	 an	 Integrated	 and	 Multidisciplinary	 Approach	 for	 the	

Advancement	 of	 Data	 Collection,	 Data	Management	 and	 Data	 Analysis	 to	 Aid	 Interpretation	 of	 Trace	

Evidence.	

Background	information:

1. Description	of	research	need:

There	 have	 been	 efforts	 to	 create	 collection/reference	 data	 sets	 in	 trace	 evidence	 materials	

(glass,	automotive	paint,	duct	tape,	fiber,	polymers	and	pigments).		The	most	comprehensive	database	

in	 use	in	 trace evidence	is	 the	 Paint	Database	Query	 (PDQ),	 that	 permits	identifying	 the	make,	model	

and	years	of	vehicles	with	paints	consistent	with	the	evidence.	It	also	permits	some	assessment	of	the	

uniqueness	of	an	association.		However,	 the	 full	potential	of	databases	has	 received	little	attention	in	

research	and	has	not	been	explored	beyond	traditional	investigative	queries.	As	a	result,	databases	are	

often	underutilized	in	forensic	and	intelligence	investigations.	

The	application	of	some	interpretation	models	(i.e.	likelihood-based	approaches)	is	restricted	in	

trace	evidence	due	to	several	factors,	including:	a)	few	existing	reference	collections	and	databases	that	

fuse	multiple	sensors	for	automated	interpretation	platforms,	b)	gaps	of	information/research	regarding	

studies	of	transfer	and	persistence	of	trace	materials,	c)	gaps	on	updated	survey	studies	and	evaluation	

of	error	rates,	d)	gaps	of	knowledge	of forensic	practitioners	in	fundamental	and	application	statistics;	d)	

gaps	of	knowledge	of statisticians/	computer	scientists	in	 fundamental	and	practical scientific/forensic	

background.

As	a	result,	an	integrated	and	multidisciplinary	approach	is	needed	 to	incorporate:	a)	strategic	

collaborations	 and	 effective	 plans	 for	 cross-training	 trace	 examiners,	 computer specialists	 and	

statisticians,	b)	updated	research	efforts	for	transfer	and	persistence	studies,	survey/population	studies,	

inter-laboratory	 validations	 and	 comparisons,	 c)	 strong	 proposals	 to	 bring	 together	 collaborative	

knowledge	 of	 examiners,	 researchers,	 statisticians,	 computer	 scientists	 and	 manufacturer’s	

organizations	 to	 develop	 and/or	 update	 relevant	 databases,	 d)	 development	 of	 automated	 platforms	

that	help	the	management	of	different	layers	of	information	to	assist	decision	making,	e)	development

of	 standard	 methods,	 dissemination	 and	 implementation	 strategies	 for	 the	 application	 of	 integrated	

solutions	for	data	interpretation.

2. Key	bibliographic	references	relating	to	this	research	need:

Note:	the	following	references	are	provided	as	an	example	of	relevant	references but	do not	represent	

an	all-inclusive	bibliographic	review	in	this	field.

1. Aitken	 C.	 G.	 G.,	 and	 D.	 Lucy.	 Evaluation	 of	 trace	 evidence	 in	 the	 form	 of	 multivariate	 data.

Journal	of	the	Royal	Statistical	Society:	Series		(Applied	Statistics),	53(1):109-122,	2004.

2. Aitken,	C.	G.	G.;	D.	Lucy,	G.	Zadora,	and	J.	M.	Curran.	Evaluation	of	transfer	evidence	for	threelevel	 multivariate	 data	 with	 the	 use	 of	 graphical	 models.	 Computational	 Statistics	 and	 Data

Analysis,	50(10):2571-2588,	2006.

3. Buckle J,	D.A.	MacDougal,	 R.R.	Grant.	A	 computerized	 system	 for	 the	identification	 of	 suspect

vehicles	involved	in	hit	and	run	accidents

4. Buckle,	J.	L.,	MacDougall,	D.	A.,	&	Grant,	R.	R.	(1997).	PDQ—Paint	Data	Queries:	The	History	and

Technology	Behind	the	Development	of	the	Royal	Canadian	Mounted	Police	Forensic	Laboratory
4

Services	Automotive	Paint	Database.	Canadian	Society	of	Forensic	Science	Journal,	30(4),	199-

212.

5. Bull	PA,	Morgan	RM,	Sagobsky	A,	Hugher	GJA.	The	transfer	and	persistence	of	trace	particulates:	

experimental	studies	using	clothing	fabrics.	Sci	and	Justice	46,	3,	2006,	185-195.

6. Cooper,	G.	The	indirect	transfer	of	glass	fragments	to	a	jacket	and	their	subsequent	persistence.	

Sci.	Justice	2013,	53,	166-170.

7. Curran	J.	M.	,	C.	M.	Triggs,	J.	R.	Almirall,	J.	S.	Buckleton,	and	K.	A.	J.	Walsh.	The	interpretation	of	

elemental	 composition	 measurements	 from	 forensic	 glass	 evidence	 ii.	 Science	 and	 Justice,	

37(4):245-249,	1997.

8. Diana	M.	Wright,	(2012)	Sourcing	Paint	Smears:	A	Hate	Crime	Highlights	the	Utility	of	the	Paint	

Data	Query	(PDQ)	Database.	Canadian	Society	of	Forensic	Science	Journal	45:2,	pages	79-88.

9. Hicks,	T.;	 Vanina,	 R.;	Margot,	 P.	Transfer	and	 persistence	 of	glass	 fragments	 on	garments.	 Sci.	

Justice	1996,	36,	101-107.

10. http://www.nist.gov/oles/forensics/forensic-database-trace-evidence-table.cfm

11. Jojodia	S,	Wijesekera	D,	eds.	Data	and	applications	security	XIX.	LNCS,	col	3654,	Springer	2005.

12. Lavine BK,	 A.	 Fasasi,	 N.	Mirjankar,	 C.	White.	 Search	 prefilters	 for	 library	 matching	 of	 infrared	

spectra	 in	 the	 PDQ	 database	 using	 the	 autocorrelation	 transformation.	 Microchem.	 J.,	 113	

(2014),	pp.	30–35

13. Lavine BK,	 A.	 Fasasi,	 N.	 Mirjankar,	 M.	 Sandercock,	 S.D.	 Brown.	 Search	 prefilters	 for	 mid-IR	

spectra	of	clear	coat	automotive	paint	smears	using	stacked	and	linear	classifiers J.	Chemom.,	28	

(2014),	pp.	385–394 Can.	Soc.	Forens.	Sci.	J.,	30	(1997),	pp.	199–212

14. Mark P, Sandercock L,	 (2013)	 75	 Years	 of	 Forensic	 Chemistry	 in	 the	 Royal	 Canadian Mounted	

Police.	A	Timeline	for	Trace	Evidence:	1937–2012.	Canadian	Society	of	Forensic	Science	Journal	

46:2,	pages	120-127.

15. Neumann	C,	Margot	P.	New	perspectives	in	the	use	of	ink	evidence	in	 forensic	science	Part	III:	

operational	applications	and	evaluation.	Forenci	Sci	Int	2009;192:29-42.

16. Neumann	 C,	Margot	 P.New	 perspectives	 in	 the	 use	 of	 ink	evidence	 in	 forensic	 science	 Part	II:	

development	 and	 testing	 of	 matemathical	 algorithms	 for	 the	 automatic	 comparison	 of	 ink	

samples	analysed	by	HPTLC.	Forenci	Sci	Int	2009;185:38-50.

17. Neumann C,	Ramotowski	R,	Genessay	T.	Forensic	examination	of	ink	by	high-performance	thin	

layer	chromatography- The	United	States	Secret	Service	Digital	Ink	Library.	J		Chromatography	A	

2011;	1218:2793-2811.

18. Olivier M.S. On	metadata	context	in	database	forensics.	Digit	Investig,	5	(3)	(2009),	pp.	115–123

19. Scheer,	S.	(2006):	`The	Evidential	Value	of	Elemental	Composition	in	Forensic	Glass	Examination:	

the	Use	 of	Multivariate	 Likelihood	 Ratio’.		A	 report	written	 for	Netherlands	Forensic	Institute,	

The	Hague.

20. Siegel	JA.	 Evidence	Value	of	Textile	Fiber- transfer	and	persistence	of	fibers.	Forensic	Sci	Review,	

1997,	9,2,	81-96

21. Trejos,	 T.;	 Koons,	 R.;	 Weis,	 P.;	 Becker,	 S.;	 Berman,	 T.;	 Dalpe,	 C.;	 Duecking,	 M.;	 Buscaglia,	 J.;	

Eckert-Lumsdon,	T.;	Ernst,	T.;	Hanlon,	C.;	Heydon,	A.;	Mooney,	K.;	Nelson,	R.;	Olsson,	K.;	Schenk,	

E.;	Palenik,	C.;	Pollock,	E.	C.;	Rudell,	D.;	Ryland,	S.;	Tarifa,	A.;	Valadez,	M.;	van	Es,	A.;	Zdanowicz,	

V.;	 Almirall,	 J.	 R.	 Forensic	 analysis	 of	 glass	 by μ-XRF,	 SN-ICP-MS,	 LA-ICP-MS	 and	 LA-ICP-OES:	

evaluation	 of	 the	 performance	 of	 different	 criteria	 for	 comparing	 elemental	 composition.	 J.	

Anal.	At.	Spectrom.	2013b,	28,	1270-1282.
5

22. Voojis	C,	Vergeer	P,	van	der	Weerd	J.	Towards	source	level	evaluation of	the	evidential	value	of

fibre	examinations.	F	Sci	International,	2015,	250:	57-67.

23. Weis,	 P.;	 Dücking,	 M.;	 Watzke,	 P.;	 Menges,	 S.;	 Becker,	 S.	 Establishing	 a	 match	 criterion	 in	

forensic	comparison	analysis	of	float	glass	using	laser	ablation	inductively	coupled	plasma	mass	

spectrometry.	J.	Anal.	At.	Spectrom.	2011,	26,	1273-1284.

24. Zadora	 G,	 Ramos	 D.	 Evaluation	 of	 glass	 samples	 for	 forensic	 purposes- an	 application	 of	

likelihood	 ratios	 and	 information-theoretical	 approach,	 Chemometrics	 and	 Intell	 Lab	 Sys	 102,	

2010,	63-83.

25. Zadora	 G.	 Evaluation	 of	 evidence	 value	 of	 glass	 fragments	 by	 likelihood ratio and	 Bayesian	

Network	approaches,	Anal.	Chim.	Acta,	2009,	642,	279-290.

26. Zadora,	 G.;	 Neocleuous	 T.	 Likelihood	 ratio	 model	 for	 classification	 of	 forensic	 evidence.	 Anal	

Chim	Acta,	2009,	266-278.