This dataset contains all the spectra and OTU table data used in the paper "Spectroscopic investigation of faeces with surface-enhanced Raman scattering: a case study with coeliac patients on gluten-free diet", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, analzye data and generate the figures shown in the paper.

Spectral data are available in 2 different format:

• the original TXT files (as generated from the Raman instrument, 1 file = 1 spectrum)

• as RData file (an hyperSpec object including metadata), directly to be opened in R

The OTU table is available either as a single XLSX file or as a RData file to be opened in R.

The R code used to generate the figures is available as a single file "Rcode.R".

Relational Event-Model (REM) predicted values used in the production of figures, given as .RData files, along with other files used for plotting ('coop_only.RData', 'Fig3_perc_store_list.RData'). Figures can be produced by loading the data files in R and running the relevant section of the MK_REMs.R script (available from https://github.com/mkings-220920/Cornish-Jackdaws).- Figure 2 = 'model2_pred_list2.RData', see code section beginning line 473 in MK_REMs.R- Figure 3 = 'Fig3_perc_store_list.RData', see code section beginning line 501 in MK_REMs.R- Figure 4 = 'model4_pred_list4.RData' and 'model5_pred_list5.RData', see code section beginning line 555 in MK_REMs.R- Figure 5 = 'coop_only.RData', see code section beginning line 611 in MK_REMs.R- Figure 6 = 'model8_pred_list8.RData', see code section beginning line 712 in MK_REMs.R- Supplementary Fig. 1 = 'coop_only.RData', see code section beginning line 743 in MK_REMs.R'model2_pred_list2.RData', 'model4_pred_list4.RData', 'model5_pred_list5.RData', and 'model8_pred_list8.RData' contain model predictions used in figures. Median predicted values and prediction intervals were calculated from these distributions of predicted values.'coop_only.RData' is a dataset that contains all of the time-varying covariates (as calculated in Eventnet), but without 'non-events' (i.e. hypothetical/expected events as generated by the permutation procedure). As it only contains the observed events, this dataframe is used at points in the 'MK_REMs' script for plotting. 'Fig3_perc_store_list.RData' is a dataset containing cumulative success/fail differential (across the duration of the experiment) for the 'non-events' in permuted datasets. See 'permuted' in Fig. 3

Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving ‘target information’ from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.