Aston Data Explorer

Tan, Mingming

Hazarika, Pratim

Forysiak, Wladek

Harper, Paul

Donodin, Aleksandr

Phillips, Ian

Ultra-wideband discrete Raman amplifier optimisation for single-span S-C-L-band coherent transmission systems

Uren, Victoria

Edwards, John S.

Coding Manual: Technology Readiness and the Organizational Journey Towards AI 
Victoria Uren & John S. Edwards 2022. 
 
 
Code Notes Examples 
TRL Based on the NASA Technology Readiness Levels with modifications 
for IS. Concerns the development of technology from principles and 
vision (level 1) through to operational use (level 9) May code the 
levels (TRL1-9) that are relevant. See TRL table below. 
“Then we do it on site with them and with real data. 
Typically, what we’ll do is we’ll do it in parallel to 
production.” [TRL 4 or 5] 
“The ones that get it, they go into the execution, so 
from probably TRL 5 through to that prototype, get the 
business buy in.” [TRL 5+] 
CMM Originating from the U.S. Department of Defense Capability Maturity 
Model CMM and refined by others including Carnegie Mellon 
University. Addresses the formality of processes within an 
organization, including those for system development and quality 
management. Has 5 levels, with a variety of labels, where 1 is the 
least mature and 5 the most. May code the levels. See figure below. 
https://en.wikipedia.org/wiki/Capability_Maturity_Model_Integration  
“how do we de-risk it? It’s more of a phased approach” 
[CMM2 perhaps?] 
“suddenly all those people, well I don’t think they’re out 
of a job right? But they’re not training chat bots 
anymore. Those businesses have basically been out 
comp... gazumped by BigITCompany.” [CMM 4&5?] 
People Refers to People in the “golden triangle” covering user needs, the 
users of solutions, developers, but also levels of management within 
an organization which need to champion change. 
“all the chief executives, all the regulatory bodies, the 
first time I think in years they’ve all sat together in the 
same room” [People] 
“I’ve really emphasised on them it being a really crucial 
part of our business because they hold the knowledge 
that the AI will never have” [People] 
Processes Refers to Processes in the “golden triangle” referring to how things 
get done, i.e. the cross functional steps needed to deliver desired 
outcomes. Two kinds of process can de distinguished: Development 
Processes, which are the processes the development organization 
uses to deliver systems, and Business Processes which the client 
organization uses to generate value. 
“the disrupter, where they’re trying to find new models 
of software and business using these techniques to 
disrupt the market. So to bring new capabilities, new 
apps, new insights takes those kind of things.” 
[Business Processes] 
“a big part of the product is around what I call weird 
thing spotter. And that is about working out when 
you’ve left the heating on overnight basically is a good 
example” [Business Process] 
“If the data was in the right shape it means somebody’s 
put it in the right shape already, which means they’ve 
done a similar piece of work.” [Development Process] 
Technology Refers to Technology in the “golden triangle” – the hardware, 
software, infrastructure, architecture etc. required to make the 
information system function. 
“So there’s a chat bot on the product and that’s 
something that’s not built by us. We borrow 
BigITCompanyName’s. Buy a ProductName2 product. 
That’s a creepily, terrifyingly good bot” [Technology] 
“the temperature sensors that we take temperature 
readings from there were installed in 1998, which is, 
what’s that? Dot com bubble right? It’s before the dot 
com bubble burst, it’s before FaceBook, it’s before 
cybersecurity was a word. It’ a really, really long time 
ago and we have to interface with all of those devices 
so you have some reliability issues” [Technology] 
Data Whatever can be stored in computer storage media, manipulated by 
computer operations or transferred via communications networks. 
Covers the features and attributes of Data but NOT what it is used for, 
which is coded as Process  
“You can’t turn around and say “give me your data 
dump! I want to do something really interesting and 
do, you know, work with my computer science team 
and let’s see what we come up with”. You can’t do 
that” [Data] 
“the understanding of data as an asset. So you have, 
um the governance around that dataset, so data 
governance as a conversation has evolved massively 
and the standard industry didn’t really keep up that 
well. So, you have your standard taxonomy based 
quality which is are things called the right thing. You 
have your availability of data concerns, you know have 
we got the right comparative granularity and those 
things. Through to how the data changes and is 
different and is difficult.  So, you know, how can it be 
true that the data looks like this when it didn’t 
yesterday?” [Data] 
Success 
Criteria 
Criteria used by a business to assess whether the deliverables of a 
project, function or process are what was asked for. In some cases, 
these may be quality metrics used to judge the acceptability of 
products or services to customers. 
“reducing the time for innovation to be adopted” 
[Success Criteria] 
“Reliability, resilience and uptime” [Success Criteria] 
“then there’s that change management so are the 
actual end users that are going to use this system, 
whether it be the end citizen or whether it be an 
employee, are they getting engaged. Are they buying 
in. Are they seeing the advantages. Are we being able 
to argue successfully against any fear factors or 
frustrations about this so it is a project that could scale. 
So I’d say we have these evolving success criterias 
through different phases of the project.” [Success 
criteria] 
Role of AI Concerns the ways in which AI is used and the kinds of human activity 
it replaces, for example automation or decision support. 
“for example in radiology, we can have a second 
viewing by a machine, rather than a peer to peer 
human, and under exceptional circumstances if they 
don’t agree then it goes to another human, erm, how 
do we streamline things like that? That’s what I’m 
interested in” [Role of AI] 
 
   
 
  
 
 
4 
Technology Readiness Levels1 
 
 
 
 
1 Based on Meystel, A et al., 2003 Performance Measures for Intelligent Systems: Measures of Technology Readiness, PERMIS’03 
 
 
Level Description Diagnostic Questions 
TRL 1 Basic Principles and Broad 
vision 
Have you researched the system 
in principle? Do you have a vision 
for the system? 
TRL 2 Conceptual design Have you proposed engineering 
components which need to be 
part of the system? Do you have a 
conceptual design for the system? 
TRL 3 Theoretical & experimental 
analysis. Proof of concept 
Have you experimented with 
subcomponents of the system? 
Have you scrutinized innovative 
components? 
TRL 4 Component validation in 
“laboratory” conditions 
Have you integrated 
subcomponents of the AI system 
to check that they will work 
together? Have you considered 
issues such as interoperability, 
maintainability, scalability, 
security etc.? 
TRL 5 Component validation in 
more realistic conditions 
Have you developed a high-fidelity 
prototype of the system with 
reasonably realistic components? 
Have you verified the prototype 
works as desired? 
TRL 6 Subsystem model or 
prototype demonstration 
Have you demonstrated a 
prototype system in a relevant 
environment (e.g. lab test with 
realistic data or test in simulated 
environment)? 
TRL 7 System prototype 
demonstration in 
operational environment 
Do you have an operational 
system that can be demonstrated 
in its operational environment? 
Are there processes in place to 
support the software? 
TRL 8 Actual system completed, 
tested and demonstrated 
Do you have a system which is in 
its final form and meets its design 
specifications? Is it ready to work 
in its intended application? 
TRL 9 Actual system proven 
through operational use 
Has the software been used under 
operational conditions for an 
extended period? Has it been 
debugged? Does it reliably 
produce the required outputs? 
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5 
Capability Maturity Model 
 
 
Source: 
https://en.wikipedia.org/wiki/Capability_Maturity_Model_Integration#/media/File:Characteris
tics_of_Capability_Maturity_Model.svg  


Technology readiness and the organizational journey towards AI adoption: An empirical study

Extended Kalman Filter Based 2D Impairment Mitigation in Nonlinear Optical Systems with Equalization Enhanced Phase Noise

Ellis, Andrew

Distributed Raman Amplification for Fiber Nonlinearity 

Compensation in a Mid-Link Optical Phase Conjugation System

Bandwidth extension in a mid-link optical phase conjugation using higher order Raman amplification with a single FBG

Schofield, Andrew

Second-order texture gratings produce overestimation of height in depictions of rectangles and steps.

Li, Yiming

Table 1: List of acronyms
AOM	Acousto-optic modulator
B2B	Back-to-back
BER	Bit error rate
CD	Chromatic dispersion
DP-QPSK	Dual-polarization quadrature phase shift keying
DSP	Digital signal processing
EVM	Error vector magnitude
FDL	Fibre delay line
FM-EDFA	Few-mode erbium-doped fibre amplifier
FMF	Few-mode fibre
FSO	Free-space optical
HD-FEC	Hard-decision forward error correction
IQ	In-phase and quadrature signals
LG	Laguerre-Gaussian
LMS	Least mean squares
LP	Linearly polarized
MDG	Mode dependent gain
MDL	Mode dependent loss
MDM	Mode-division multiplexing
MIMO	Multiple-input multiple-output
MMSE	Minimum mean square error
MSPL	Mode-selective photonic lantern
OAM	Orbital-angular-momentum
OSNR	Optical signal-to-noise ratio
PC	Polarization controller
SIC	Successive interference cancellation
TDM	Time-division multiplexing
VOA	Variable optical attenuator




Mode division multiplexing free space optical transmission

Noor, Shabnam

Stones, Jeffrey S

Li, Ming Jun

E-, S-, C- and L-band coherent transmission with multistage discrete Raman amplifier

Roberts, Brian

Summers, Robert

Effects of stimulus naturalness and contralateral interferers on lexical bias in consonant 
identification 
 
Datasets 
These datasets comprise listeners’ judgments of voicing for the initial consonant of the sets of syllables 
used in the experiments described in the article of the same title (Roberts, Summers, and Bailey, 2022; 
Journal of the Acoustical Society of America). There are two spreadsheets for each experiment. One 
spreadsheet contains listeners’ categorization responses for the initial consonant ([g] or [k]), averaged 
over 10 repetitions, from which the identification functions and Ganong shifts are computed; the other 
contains the response category and reaction time for each individual trial, from which the reaction-
time profiles and overall reaction times are computed. These data can also be used to compare the 
reaction times for word and non-word responses. Note that the vocoded versions of the syllables were 
made from reference stimuli that included a leading 40-ms silence (see main article) which must be 
deducted from the notional reaction time measured to obtain the correct value. 
 
Each spreadsheet comprises two summary worksheets, the first of which includes relevant 
demographic information, and individual worksheets for each listener. Each summary worksheet 
contains aggregated responses and computed measures for each condition (for details, see below). 
Subsequent worksheets comprise the raw data for each listener in each condition, either for each 
stimulus when aggregated across the ten repetitions (Ganong shifts) or for each trial (reaction times). 
Experiment 1 
Ganong shifts 
The first worksheet (ALL) contains relevant demographic information and the computed Ganong shifts 
for each listener (and averaged across listeners) in each condition—reference (REF), modulated noise 
band (MNB), and modulated sine band (MSB). The second worksheet (Summary) computes the mean 
Ganong shifts across listeners (Column K, bold) in each condition from the mean differences in the 
identification functions at each continuum position in the two contexts (“_ISS” and “_IFT”). 
The raw data in the subsequent worksheets comprise: (a) stimulus ID [Column heading: Stim], (b) 
stimulus type (REF, MNB, or MSB), (c) number of [k] responses, (d) number of trials (always 10 here), 
(g) continuum step (1-8), (h) proportion of [k] responses in “_IFT” context, (i) proportion of [k] 
responses in “_ISS” context, (j) difference score (and averaged over all 8 positions to give the Ganong 
shift in percent). 
Reaction times 
The first worksheet (ALL) contains relevant demographic information and the computed mean of 
median reaction times for each listener (and averaged across listeners) in each condition (REF, MNB, 
and MSB). The second worksheet (Summary) computes the mean of median reaction times across 
listeners in each condition for each continuum position and context (“_ISS” and “_IFT”). 
The raw data in the subsequent worksheets comprise: (a) stimulus ID [Column heading: Stim], (b) 
stimulus type (excitation source) for condition (always REF in this case) [Type], (c) response category 
([g] or [k]), (d) reaction time (ms), (g) computed median reaction time (ms), (i) stimulus ID [Stim], (j) 
excitation source for condition (always MNB in this case) [Type], (k) response category ([g] or [k]), (l) 
uncorrected reaction time (ms), (m) corrected reaction time (subtracts 40 ms), (o) computed median 
reaction time (ms), (q) stimulus ID [Stim], (r) excitation source for condition (always MSB in this case) 
[Type], (s) response category ([g] or [k]), (t) uncorrected reaction time (ms), (u) corrected reaction time 
(subtracts 40 ms), (w) computed median reaction time (ms). 
Experiment 2 
Ganong shifts 
The first worksheet (Summary) contains relevant demographic information and the computed Ganong 
shifts in each spatial condition (MNBF and MNBR, target ear = fixed or random)—and collapsed across 
them—in each interference condition (C1-C4) for each listener. The second worksheet 
(SummaryContinua) treats spatial condition in the same way and computes the mean Ganong shifts 
across listeners in each interference condition from the mean differences in the identification functions 
at each continuum position in the two contexts (“_ISS” and “_IFT”). 
The raw data in the subsequent worksheets comprise: (a) stimulus ID [Column heading: Stim], (b) 
spatial condition (MNBF = fixed-ear target or MNBR = random-ear target), (c) number of [k] responses, 
(d) number of trials (always 10 here), (f) identifies spatial condition (MNBF or MNBR) and interference 
condition (C1-C4), (g) continuum step (1-8), (h) proportion of [k] responses in “_IFT” context, (i) 
proportion of [k] responses in “_ISS” context, (j) difference score (and averaged over all 8 positions to 
give the Ganong shift in percent), (m) collapsed case: identifies interference condition (C1-C4), (n) 
collapsed case: continuum step (1-8), (o) collapsed case: proportion of [k] responses in “_IFT” context, 
(p) collapsed case: proportion of [k] responses in “_ISS” context, (q) collapsed case: difference score 
(and averaged over all 8 positions to give the Ganong shift in percent). 
Reaction times 
The first worksheet (Summary) contains relevant demographic information and the computed mean of 
median reaction times in each spatial condition (MNBF or MNBR, target ear = fixed or random)—and 
collapsed across them—in each interference condition (C1-C4) for each listener. The second worksheet 
(SummaryContinua) treats spatial condition in the same way and computes the mean of median 
reaction times across listeners in each interference condition for each continuum position and context 
(“_ISS” and “_IFT”). 
The raw data in the subsequent worksheets comprise: (a) stimulus ID [Column heading: Stim], (b) 
spatial condition (always MNBF—fixed-ear target—in this case), (c) response category ([g] or [k]), (d) 
uncorrected reaction time (ms), (e) corrected reaction time (subtracts 40 ms), (f) ear of presentation 
(target, interferer), (g) computed median reaction time (ms), (i)-(l) median and mean-of-median 
reaction times in MNBF case for C1-C4, (n) stimulus ID [Stim], (o) spatial condition (always MNBR—
random-ear target—in this case), (p) response category ([g] or [k]), (q) uncorrected reaction time (ms), 
(r) corrected reaction time (subtracts 40 ms), (s) ear of presentation (target, interferer), (t) computed 
median reaction time (ms), (v)-(y) median and mean-of-median reaction times in MNBR case for C1-C4, 
(aa) spatial condition (always collapsed across MNBF and MNBR cases), (ac)-(af) median and mean-of-
median reaction times for C1-C4 when collapsed across spatial condition. 
 
 


Effects of stimulus naturalness and contralateral interferers on lexical bias in consonant identification

Rothnie, Alice

Patel, Jaimin

Allen, Marcus

Pollock, Naomi

Maher, Jacqueline

Data underpinning 'The function of BK channels extracted and purified within SMALPs'

Aston University

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