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MMC S1. Parameter estimation are presented. Parameters of the control dataset using the first model (S1). Parameters of the treated dataset (S2) using the first model. Parameters obtained when estimating the second model in the control dataset (S3). Parameters obtained when estimating the second model in the treated dataset (S4)., Peer reviewed

The two attached Excel sheets contain information about the datasets collected by the Early Stage Researchers (ESRs) in the RNAct MSCA-ITN project (RNAct_datasets.xlsx) and on the questionnaire that was put to the ESRs in relation to the Data Science Wizard (DSW) knowledge model that was developed as part of RNAct. The zip file contains data in relation to the Data Science Wizard template development: - DMP_1stRound_introductory_presentation.pdf: Presentation for the ESRs to prepare them for filling in the first version of the DMP - DMPs_1stRound, DMPs_2ndRound: The filled in DMPs by the ESRs in the first and second round - RNAct-ESRtraining-KM_1.0.3.km: The first version of the knowledge model to create the DMP - RNAct-ESRtraining-KM_1.0.14.km: The second and final version of the knowledge model to create the DMP - TemplateDMP_RNAct-ESRtraining-KM_1.0.14.pdf: A PDF overview of the second and final version of the knowledge model, RNAct – Enabling proteins with RNA recognition motifs for synthetic biology and bio-analytics. 813239 European Commission, No

Contents: Clustal_X species and raw alignment: clustal_x_aln_species.fa clustal_aligment_supplementary.docx Data for phylogenetic pipeline: Species included in the eukaryotic database with download link eukaryotic_database__data.xlsx Query for blastsearch HOMSAP007601_serrs.fa Raw blastoutput SRS2_SLIMP_blastoutput.txt Fasta sequences of all sequences used in the phylogenetic analyses SRS2_SLIMP_species.fa Raw alignment of blasthits SRS2_SLIMP.aln Trimmed alignment SRS2_SLIMP_trim10.aln Raw tree files SRS2_SLIMP_trim10.aln.ckp.gz SRS2_SLIMP_trim10.aln.contree SRS2_SLIMP_trim10.aln.iqtree SRS2_SLIMP_trim10.aln.log SRS2_SLIMP_trim10.aln.mldist SRS2_SLIMP_trim10.aln.model.gz SRS2_SLIMP_trim10.aln.nex SRS2_SLIMP_trim10.aln.treefile SRS2_SLIMP_trim10.aln.uniqueseq.phy SRS2_SLIMP_trim10.newick.txt, PID2019-108037RB-100, Peer reviewed

[Description of methods used for collection/generation of data] The three colon reactors (R1, R2 and R3) of the BFBL gut simulator were inoculated (1%) with child (5-7 years old) pooled fecal sample. After the microbiota stabilization stage, carrageenan was added to the system at increasing concentrations (0.3 g/L, 1 g/L, 2 g/L and 3 g/L), three times per day for 7 days for each concentration (samples CGN). In addition, another run without addition of carrageenan to the BFBL gut simulator was carried out for the same period of time to assess changes due to the evolution of the bacterial populations over time under these experimental conditions (samples C). During the experimental set up, samples were collected every 24 h from the three colon reactors and centrifuged (11,200 ×g during 10 min at 4 °C), and the pellet and supernatant were stored separately. Bacterial genomic DNA from the pellet was extracted according to the manufacturer’s instruction of the EZNA® Bacterial DNA Kit (Omega Biotek) and using a FastPrep equipment for mechanical lysis (Bio 101 FastPrep FP120, Savant Instruments). DNA samples were sent to Novogene Europe (Cambridge, UK) for sequencing the V3-V4 region of the 16S rRNA gene amplified by using 341F (5′- CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGA CTACNNGGGTATCTAAT-3′) primers. The PCR products were sequenced on a paired-end Illumina platform (NovaSeq 6000, Illumina) to generate 250 bp paired-end raw reads., The aim of this work was to determine the possible impact of carrageenan on intestinal microbiome, and the impact of this carrageenan -fed microbiome on the intestinal barrier function. Thus, carrageenan was added at increasing doses (in the range 0.3-3 g/L) 3 times daily, for one week for each dose, to a pool of stabilized infant faecal microbiota under stable conditions in the BFBL simulator of the colonic ecosystem. Samples were collected daily from the three colonic reactors. The microbiota composition was analysed by 16S rDNA amplicon-based metagenomics. The impact of this additive on the intestinal barrier function was approached by measurement of Transepithelial Electrical Resistance, the Lucifer Yellow paracellular flux through the Caco-2 cells and the expressions of tight junctions’ proteins., MCIN/ AEI /10.13039/501100011033 (project reference: PID2019- 382 106071RB-I00)., Peer reviewed

-Additional analysis and calculations, including a figure with the types of defects and calculations of the local density of states and the Seebeck coefficients. -Transparent Peer Review report available., Peer reviewed

[Description of methods used for collection/generation of data] To validate the assumption of spatial independence, we extracted the residuals from the model and plotted them in space. In an ideal scenario, the pattern of residuals should be random to consider that there is no spatial autocorrelation remaining in the model. Additionally, to assess the spatial variability within model residuals, we fitted a semivariogram (Zhang et al., 2009). A flat semivariogram (without sharp increases) of the residuals would indicate the absence of spatial autocorrelation, Results of the residuals analyses of the species distribution models from crustaceans, small pelagic and benthopelagic fishes from South America region, Southern African region and Australia and New Zealand region, Peer reviewed

Complementary content for the main text, including dependence of Fourier MOKE maps on materials parameters and linearly polarized light, as well as details of the fitting algorithm and its uniqueness., Peer reviewed

This dataset is composed of the files required for simulating laboratory-scale injection of CO2-saturated water and HCl solution into cm-scale limestone samples. Six files required for simulating the CO2 injection experiment are included in the folder named CO2. The names of these files are as follows: “CO2-input”: input file read by the Crunchflow code and providing the necessary physical and chemical parameters needed for the simulation -these parameters mainly include time intervals, discretization, initial and boundary conditions, geochemical conditions-; “database”: geochemical database for running the simulation; “64bit-libiomp5md”: A dynamic link library (DLL) required to run the Crunchflow code; “Perm.in.x”: permeability file in X direction for all grids; “Perm.in.y”: permeability file in Y direction for all grids; “Perm.in.z”: permeability file in Z direction for all grids. The same files for simulating the HCl injection experiment are included in the folder named HCl. This folder includes two input files “HCl-input-1” and “HCl-input-2” (instead of one) required for simulating the two stages of HCl solution injection into the limestone sample. To run the simulations, one needs to execute the Crunchflow executable file and enter the name of the input file, i.e., either “CO2-input” or “HCl-input-1” with the suffix “.in” in the command window and press enter. Note that in the HCl injection case, you only need to run the “HCl-input-1” file. The second input file will be read at the proper time by the keyword “later_inputfiles HCl-input-2.in” defined in the “HCl-input-1”., Peer reviewed

Research conducted in the framework of MIRACLE Project (Photonic Metaconcrete with Infrared RAdiative Cooling capacity for Large Energy savings, GA 964450), coordinated by Dr. Jorge Sánchez Dolado, from Centro de Física de Materiales (CFM)., [The OPC emissivity has been calculated with Transfer-Matrix-Method applied to a slab having roughness RMS and complex permittivity obtained by applying the effective medium theory of Slovick (Slovick, B. A. Negative Refractive Index Induced by Percolation in Disordered Metamaterials. Physical Review B 95, 094202 (2017)) to the OPC microstructure calculated with µic (https://micepfl.sourceforge.net/) and using the complex permittivity obtained by CSIC with GULP (https://gulp.curtin.edu.au/) for the homogeneous phases. This is described in detail in Cagnoni, M., Tibaldi, A., Dolado, J. S. & Cappelluti, F. Cementitious Materials as Promising Radiative Coolers for Solar Cells. iScience 25, 105320 (2022).], [The solar cell operating temperature when coupled to the OPC based radiative cooler has been obtained by means of the detailed-balance model commonly employed in the literature. This is discussed in detail in Cagnoni, M., Tibaldi, A., Dolado, J. S. & Cappelluti, F. Cementitious Materials as Promising Radiative Coolers for Solar Cells. iScience 25, 105320 (2022).], This dataset contains information of the behavior of temperature of single-junction solar cells with Ordinary Portland Cement radiative cooler., - The OPC emissivity has been calculated with Transfer-Matrix-Method applied to a slab having roughness RMS and complex permittivity obtained by applying the effective medium theory of Slovick (Slovick, B. A. Negative Refractive Index Induced by Percolation in Disordered Metamaterials. Physical Review B 95, 094202 (2017)) to the OPC microstructure calculated with µic (https://micepfl.sourceforge.net/) and using the complex permittivity obtained by CSIC with GULP (https://gulp.curtin.edu.au/) for the homogeneous phases. This is described in detail in Cagnoni, M., Tibaldi, A., Dolado, J. S. & Cappelluti, F. Cementitious Materials as Promising Radiative Coolers for Solar Cells. iScience 25, 105320 (2022). - The solar cell operating temperature when coupled to the OPC based radiative cooler has been obtained by means of the detailed-balance model commonly employed in the literature. This is discussed in detail in Cagnoni, M., Tibaldi, A., Dolado, J. S. & Cappelluti, F. Cementitious Materials as Promising Radiative Coolers for Solar Cells. iScience 25, 105320 (2022)., European Commission: 964450, Peer reviewed

Research conducted in the framework of MIRACLE Project (Photonic Metaconcrete with Infrared RAdiative Cooling capacity for Large Energy savings, GA 964450), coordinated by Dr. Jorge Sánchez Dolado, from Centro de Física de Materiales (CFM)., We measured the reflectivity of Portlandite fine powder across a frequency range of 0.36-15 microns. The results are a combination of two datasets: one measured by a portable spectrophotometer (410-Solar, Surface Optics Corp.) and the other by an FTIR spectrometer (Jasco 6300) with an integrated sphere (PIKE). We extracted the data using SolarData and SpectraManager software, then merged it with KaleidaGraph software. The final merged data were exported to a .txt file., European Commission: 964450, Peer reviewed