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MD simulation, chemical synthesis, STD NMR studies, and biochemical studies., Peer reviewed

Complete NBO results and Cartesian coordinates of all optimized systems., Peer reviewed

This dataset is related to article: http://hdl.handle.net/10261/374539, This paper presents a study of sodium-decorated circumcircumcoronene, a large polycyclic aromatic hydrocarbon (PAH) regarded as a graphene prototype, and the complexes formed upon aggregation of hydrogen molecules on this substrate. Accurate force fields based on high level electronic structure calculations have been built using a rigid rotor model for the hydrogen molecules as well as a corresponding pseudo-atom approximation, aimed to pursue larger compounds where a simpler treatment could be helpful. Structural and energy features of the hydrogen adsorption have been then analyzed by means of quantum Monte Carlo methods. A large stabilization of the adsorption energy is found for up to four H2 molecules attached to the alkali atom, in agreement with conclusions reached from previous studies on smaller Na-decorated PAHs. Adsorption patterns are examined in detail as the number of attached molecules is increased, comparing with results on smaller substrates and analyzing the effects of rotational orientation by contrasting the rigid rotor vs. pseudo- atom approaches., Peer reviewed

[Description of methods used for collection/generation of data] Collection and selection of ceramic samples: More than 6000 sherds were collected from both the excavation and the survey, with approximately 3700 diagnostic sherds coming from the survey and 325 diagnostic sherds from the excavations. The majority of these sherds are medium coarse and coarse ware with lower numbers of comb-impressed pottery, porcelain and as well as more than 500 Bahla ware sherds. From the latter, 32 sherds (10 from excavation contexts, 22 surface material collected during the surveys) were chosen for scientific analyses. Petrography: All 32 samples were subjected to petrographic analysis. Petrographic analysis was used to determine the petrological and mineralogical composition of the ceramic fabrics, as well as their texture. For the analysis of the samples with ceramic petrography, small sherds were taken from each sample and prepared as thin sections in the laboratories of the School of Archaeology and Ancient History at the University of Leicester (UK) . The thin sections were then analysed on a microscope with a polariser, a ZEISS AXIOSCOPE 5 POL, in the same laboratories. Images were collected with a ZEISS AXIOCAM 208. Chemical analysis: The chemical analysis of ceramics aimed to determine the chemical composition of the ceramic bodies of a sub-set of 28 samples. It was determined with wavelength dispersive X-ray fluoresce (WD-XRF) analysis, conducted at the Fitch Laboratory of the British school at Athens. The analysis was carried out on a BRUKER S8 TIGER with a 4kW Rh X-ray tube instrument. All details regarding this instrument can be found in the paper Georgakopoulou, M., Hein, A., Müller, N., & Kiriatzi, E. 2017. Development and calibration of a WDXRF routine applied to provenance studies on archaeological ceramics. X-Ray Spectrometry 46(3): 186–199 SEM-EDS: A subset of 15 samples was subjected to scanning electron microscopy with energy dispersive spectrometry (SEM-EDS). This was used for the chemical analysis of glazes and ceramics as well as and high-resolution imaging. The SEM instrument Zeiss EVO 15 that operates with an attached EDS Oxford Instruments Ultim Max EDS with a 65 SSD detector was used at the Archaeological Science Laboratories of the Cyprus Institute. The analysis was run in high vacuum conditions, at an accelerated voltage of 20 kV, working distance 8.50 mm, process time 4 and acquisition time 30s livetime., [Methods for processing the data] Petrography: Fabrics were described according to a methodology of analysis and grouping developed in Whitbread, I.K. 1995. Greek transport amphorae, a petrological and archaeological study. Fitch Laboratory Occasional Paper, 4. Athens: British School at Athens; Whitbread, I.K. 2001. Ceramic petrology, clay geochemistry and ceramic production – from technology to the mind of the potter. Pages 449-459 in D.R. Brothwell & A.M. Pollard (eds), Handbook of archaeological sciences. Chichester: John Wiley & Sons; Quinn, P.S. 2022. Thin section petrography, geochemistry & scanning electron microscopy of archaeological ceramics. Oxford: Archaeopress. Chemical analysis: Table CHEM1 provides raw data. SEM-EDS: Data presented in Tables SEM1-4 are given in the form of mean and standard deviation for each sample, calculated based on at least 3 measurements (for each sample the number of measurements is given in parenthesis)., This is a dataset coming from the analysis of 32 Bahla ware sherds collected in archaeological activities (survey and excavation) in Central Oman, with an extended chronology of 15 to 20th centuries. The dataset includes petrographic analysis of ceramic bodies and glazes (microphotographs and fabric descriptions), WD-XRF chemical analysis of bodies (table with raw chemical data) and SEM-EDS analysis of ceramic bodies and glazes (tables with processed data, images)., Lost Cities Project, Gerda Henkel Foundation (AZ 05/LC/19). School of Archaeology and Ancient History, University of Leicester (preparation of thin sections for petrography and analysis). A.G. Leventis Foundation (preparation of polished blocks and SEM-EDS analysis)., Zivkovic et al README: this file Zivkovic et al 1 Petrography fabric: simple text description Zivkovic et al 1 Petrographic images list and captions CSV: list of captions in CSV file Zivkovic et al 1 Petrographic images list and captions XCL: list of captions in Excel file Zivkovic et al 1 Petrography microimages: ZIP file contianing all petrographic images and associated metadata Zivkovic et al 2 Chemical Analysis Caption Table: Caption in simple text Zivkovic et al 2 Table CHEM1 XCL: Table of chemical data in Excel file Zivkovic et al 2 TABLE CHEM1 CSV: Table of chemical data in CSV file Zivkovic et al 3 SEM-EDS Captions: Captions of SEM-EDS Tables in simple text Zivkovic et al 3 SEM-EDS figs: ZIP File containing all images of SEM-EDS Zivkovic et al 3 SEM-EDS Table 1 XCL: Table of SEM_EDS data in Excel file Zivkovic et al 3 SEM-EDS Table 2 XCL: Table of SEM_EDS data in Excel file Zivkovic et al 3 SEM-EDS Table 3 XCL: Table of SEM_EDS data in Excel file Zivkovic et al 3 SEM-EDS Table 4 XCL: Table of SEM_EDS data in Excel file Zivkovic et al 3 SEM-EDS Table 1 CSV: Table of SEM_EDS data in CSV file Zivkovic et al 3 SEM-EDS Table 2 CSV: Table of SEM_EDS data in CSV file Zivkovic et al 3 SEM-EDS Table 3 CSV: Table of SEM_EDS data in CSV file Zivkovic et al 3 SEM-EDS Table 4 CSV: Table of SEM_EDS data in CSV file, Peer reviewed

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Synthesis details and ligand exchange protocol for AuNRs and AuSs; schematic demonstrating the experimental setup; UV–vis–NIR, TEM measurements for AuSs and AuNR, and control experiments., Peer reviewed

In the SM, we included supporting information on the calculations and model used to calculate the photon statistic function g2(t). This model and all its details/discussions have already been published in Phys. Rev. A 104, 013719 (2021). We have also included a small section where we provide proof of the robustness of our proposal with changes in detuning and drivings., Peer reviewed

Additional theory and calculations complementing the content of the main text., Peer reviewed

In this supplementary information, we show sets of data and discussion that are complementary to those shown in the main text., Peer reviewed

[Description of methods used for collection/generation of data] The corpus statistics and methods are explained in the following article: Federico Ortega-Riba, Leonardo Campillos-Llanos, Doaa Samy (2025) "Lexical Simplification in Spanish Texts For Patients: The Complex Word Identification Task". (Under review). [Methods for processing the data] Manual annotation of complex words (CW) according to the criteria defined in the guideline explained in the companion article., We greatly thank the following colleagues who doubly revised a subset of texts in order to compute the inter-annotator agreement: Ana R. Terroba-Reinares (Fundación Rioja Salud) [ORCID: 0000-0003-1582-6481]; Ana Valverde-Mateos (Unidad de Terminología Médica, Real Academia Nacional de Medicina de España) [ORCID: 0000-0003-1610-0770]., The corpus is made up of 225 texts in Spanish annotated with complex words (CW). It contains three text types: consent forms (75 texts), clinical trial announcements (75 texts) and patient information documents (75 texts). This resource is aimed at training models, evaluating and performing experiments on complex word identification of Spanish medical texts., The corpus contains three text types: 1. Consent forms (75 texts), 2. Clinical trial announcements (75 texts) y 3. Patient information leaflets (75 texts)., This dataset was collected in the CLARA-MeD project (PID2020-116001RA-C33), with funding from the Spanish government by MICIU/AEI/10.13039/501100011033/, in project call: “Proyectos I+D+i Retos Investigación”., The corpus is made up of 225 texts. It is aimed at training models, evaluating and performing experiments on complex word identification of Spanish medical texts. The corpus contains three text types: • Consent forms (75 texts) • Clinical trial announcements (75 texts) • Patient information leaflets (75 texts) - ANN: Contains BRAT annotated files (.ann) and corresponding text files (.txt): These are separated in three folders and subfolders (corresponding to each text type): • TRAIN: ▫ ci: 51 consent forms ('consentimientos informados') ▫ eudract: 51 clinical trial announcements from REEC and EudraCT ▫ info: 51 patient-oriented information leaflets • DEV: ▫ ci: 9 consent forms FALTA UNO ▫ eudract: 9 clinical trial announcements ▫ info: 9 patient-oriented information leaflets • TEST: ▫ ci: 15 consent forms ▫ eudract: 15 clinical trial announcements ▫ info: 15 patient-oriented information leaflets - JSON files for transformer models: These are separated in TRAIN, DEV and TEST. - CSV files with the processed data, corresponding to TRAIN, DEV and TEST data. These were used for the machine learning experiments. Each file has the following fields: • Token • Label: it encodes the class (CW, 'complex word') and if the token is the Beginning of the entity (B), if it is Inside (I) or Outside (O)., Peer reviewed