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Processed data used for figure generation (e.g., averaging, normalization, baseline subtraction, or smoothing) are included alongside the raw data to ensure transparency and reproducibility.\r\n\r\nAbsorptance.xlsxAbsorptance.xlsx contains the spectroscopic data underlying Figure 1b and Figure S1 of the manuscript. Total reflectance and total transmittance spectra were measured using an Agilent Cary 5000 UV–Vis–NIR spectrophotometer. The file is organized into separate worksheets for measured and derived quantities.The worksheet “R” provides raw total reflectance measured from 2500 to 1500 nm with a 1 nm step. Below 1500 nm, reflectance is not experimentally accessible under the present configuration and is therefore taken as zero in the absorptance calculation. The worksheet “T” provides raw total transmittance measured from 2500 to 250 nm with a 1 nm step. Wavelength is given in nanometres and reflectance and transmittance are reported in %. The worksheet “A” contains total absorptance calculated as A(λ) = 1 − R(λ) − T(λ) directly from the measured data. The worksheet “Figure1b” and \"FigureS1\" contains the 1900–2500 nm spectral window used to generate Figure 1b and FigureS1, respectively. Absorptance smoothing was performed in Origin using adjacent averaging (window = 50 points, weighted average off, boundary condition reflect) for visualization only as minor point-to-point fluctuations may reduce visual clarity.\r\n\r\nRaman.xlsxRaman.xlsx contains all Raman spectroscopy data underlying Figure 2(b,c) and Figure S3(a,b) of the manuscript. Raman measurements were performed using a WITec alpha300 RA+ system with 532 nm excitation and a 50× objective lens. The file includes both raw and processed spectra.The worksheet “Ti coated Ge” contains all raw spectra measured from the initial Ti-coated Ge samples. Each sample was measured multiple times to ensure reproducibility. The worksheet “Bare Ge” contains all raw spectra measured from the initial bare Ge samples. The worksheet “Background” contains a single spectrum acquired with the excitation light switched off, used for background subtraction.The worksheet “Figure2bc” contains the statistical results presented in Figure 2b and Figure 2c. The worksheets “FigureS3a” and “FigureS3b” contain the spectra (only one characteristic spectrum is chosen for each processing condition) presented in Figure S3a and Figure S3b, respectively. Spectra shown in these figures are normalized to the main Ge Raman peak. Raw spectra prior to normalization are retained in the corresponding raw-data worksheets.\r\n\r\nXPS.xlsxXPS.xlsx contains raw X-ray photoelectron spectroscopy (XPS) data for the four samples described in the manuscript. For each sample, measurements were performed at five randomly selected positions using a 300 µm spot size. At each position, spectra were acquired with ten accumulations and averaged by the instrument.The worksheets “Ti2p”, “Ti3p”, and “Ge2p” correspond to the spectra presented in Figure 2e, Figure S4a, and Figure S4b, respectively. The spectra used to generate these figures were obtained by averaging the five positional measurements for each sample, followed by baseline subtraction. Raw spectra prior to averaging and baseline correction are retained in the file.\r\n\r\nXRD.xlsxXRD.xlsx contains the X-ray diffraction (XRD) spectra underlying Figure 2d of the manuscript. The spectra are provided in their original measured form without post-processing.Addtionally, the worksheets “Ti coated Ge” and “Bare Ge” include all raw XRD spectra acquired from Ge samples with and without Ti coating after each processing step, including rapid thermal annealing (RTA) at temperatures between 400 and 600 °C. The data are presented as measured.\r\n\r\nSEM.7zSEM.7z contains the scanning electron microscopy (SEM) images underlying Figure 2a and Figure S2 of the manuscript. Images are provided in TIFF format (1024 × 768 pixels) and were acquired using a ZEISS SUPRA 40 scanning electron microscope.Acquisition metadata, including scale bar, accelerating voltage (EHT), and date and time of capture, are embedded directly within the image files. The images are provided in their original resolution without modification.\r\n\r\nLicenseThis dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). Users are free to share and adapt the material for any purpose, provided appropriate credit is given to the original authors and the associated publication."
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"en": "The role of residential urban form and built environment in supporting social interaction, health, and well-being: a focus on forming and maintaining ties"
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"en": "These are atomic-scale models of disordered carbon-based materials generated using molecular augmented dynamics (MAD) using experimental constraints (X-ray diffraction, neutron diffraction, X-ray photoelectron spectroscopy) and a control simulation protocol based on regular molecular dynamics (MD) without the experimental constraints.\r\n\r\nThe materials include pure carbon forms:\r\n\r\n\r\n\r\ntetrahedral amorphous carbon (ta-C);\r\n\r\nglassy carbon;\r\n\r\nnanoporous carbon;\r\n\r\n\r\nas well as binary mixtures of carbon compounds:\r\n\r\n\r\n\r\ndeuterated/hydrogenated amorphous carbon (a-C:D, a-C:H);\r\n\r\noxygen-rich amorphous carbon (a-COx).\r\n\r\n\r\nFor a-C:H, experimental a-C:D data was used in the structure generation because of the difficulty in matching the available a-C:H data due to the non-trivial interaction of hydrogen's most common isotope (protium, 1H) with neutrons. Thus, available experimental data with deuterium (2H) was used instead. The XYZ structures labeled as \"aCD\" and \"aCH\" are the same, generated with target a-C:D data, with deuterium (\"D\") or hydrogen (\"H\") labels, respectively. This is to ensure the XYZ files can be imported with common atomistic manipulation software that might give errors with \"D\" labels.\r\n\r\nThe structures where generated with the TurboGAP code (turbogap.fi) using GAP potentials trained for pure-C [1], C-H mixtures [2], C-O mixtures [3] and a core-electron binding energy model trained for C-H-O [3, 4]. The methodology used is a modified Hamiltonian dynamics approach [3] whose extension to analytical forces is known as MAD, with all the details provided in the following pair of papers:\r\n\r\n\r\n\r\nT. Zarrouk and M.A. Caro. Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design. arXiv:2508.17132\r\n\r\nT. Zarrouk and M.A. Caro. Linear-scaling calculation of experimental observables for molecular augmented dynamics simulations. arXiv:2509.22388\r\n\r\n\r\nA gallery of the structures, plotted using ASE [5], Ovito [6] and ase_tools [7], is provided with file name \"gallery.png\". All other files use an obvious naming ocnvention.\r\n\r\nThe experimental data were obtained from the literature [8-12]. All the details are given in the reference papers [13, 14].\r\n\r\nContact\r\n\r\nYou can get in contact with either or both the authors at firstname.lastname(at)aalto.fi\r\n\r\nFunding\r\n\r\nThe authors acknowledge financial support from the Research Council of Finland under projects 330488, 347252, 352484, 355301 and 364778, from the European Union’s M-ERA.NET 3 program (NACAB project under grant agreement No 958174). We acknowledge the EuroHPC Joint Undertaking for awarding this project (XCALE innovation study within the Inno4scale project under grant agreement No 101118139) access to the EuroHPC supercomputer LUMI, hosted by CSC (Finland) and the LUMI consortium through a EuroHPC Regular Access call. The authors also acknowledge other computational resources from CSC – the Finnish IT Center for Science and Aalto University’s Science-IT project.\r\n\r\nReferences\r\n\r\n\r\n\r\nH. Muhli, X. Chen, A. P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M. A. Caro. Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60. Phys. Rev. B 104, 054106 (2021).\r\n\r\nR. Ibragimova, M. S. Kuklin, T. Zarrouk, and M. A. Caro. Unifying the Description of Hydrocarbons and Hydrogenated Carbon Materials with a Chemically Reactive Machine Learning Interatomic Potential. Chem. Mater. 37, 1094 (2025).\r\n\r\nT. Zarrouk, R. Ibragimova, A. P. Bartók, and M. A. Caro. Experiment-driven atomistic materials modeling: A case study combining x-ray photoelectron spectroscopy and machine learning potentials to infer the structure of oxygen-rich amorphous carbon. J. Am. Chem. Soc. 146, 14645 (2024).\r\n\r\nD. Golze, M. Hirvensalo, Hernández-León P., A. Aarva, J. Etula, T. Susi, P. Rinke, T. Laurila, and M. A. Caro. Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining DFT and GW. Chem. Mater. 34, 6240 (2022).\r\n\r\nA. H. Larsen, J. J. Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dulak, J. Friis, M. N. Groves, B. Hammer, C. Hargus, E. D. Hermes, P. C. Jennings, P. B. Jensen, J. Kermode, J. R. Kitchin, E. L. Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J. B. Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiøtz, O. Schütt, M. Strange, K. S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, and K. W. Jacobsen. The Atomic Simulation Environment – A Python library for working with atoms. J. Phys.: Condens. Matter 29, 273002 (2017).\r\n\r\nA. Stukowski. Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool. Modelling Simul. Mater. Sci. Eng. 18, 015012 (2009).\r\n\r\ngithub.com/mcaroba/ase_tools\r\n\r\nZ. Zeng, L. Yang, Q. Zeng, H. Lou, H. Sheng, J. Wen, D. J. Miller, Y. Meng, W. Yang, W. L. Mao, and H.-K. Mao. Synthesis of quenchable amorphous diamond. Nat. Commun. 8, 322 (2017).\r\n\r\nK. W. R. Gilkes, P. H. Gaskell, and J. Robertson. Comparison of neutron-scattering data for tetrahedral amorphous carbon with structural models. Phys. Rev. B 51, 12303 (1995).\r\n\r\nT. M. Burke, R. J. Newport, W. S. Howells, K. W. R. Gilkes, and P. H. Gaskell. The structure of a-C:H(D) by neutron diffraction and isotropic enrichment. J. Non-Cryst. Solids 164, 1139 (1993).\r\n\r\nC. A. Santini, A. Sebastian, C. Marchiori, V. P. Jonnalagadda, L. Dellmann, W. W. Koelmans, M. D. Rossell, C. P. Rossel, and E. Eleftheriou. Oxygenated amorphous carbon for resistive memory applications. Nat. Commun. 6, 1 (2015).\r\n\r\nA. C. Forse, C. Merlet, P. K. Allan, E. K. Humphreys, J. M. Griffin, M. Aslan, M. Zeiger, V. Presser, Y. Gogotsi, and C. P. Grey. New insights into the structure of nanoporous carbons from NMR, Raman, and pair distribution function analysis. Chem. Mater. 27, 6848 (2015).\r\n\r\nT. Zarrouk and M. A. Caro. Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design. arXiv:2508.17132.\r\n\r\nT. Zarrouk and M. A. Caro. Linear-scaling calculation of experimental observables for molecular augmented dynamics simulations. arXiv:2509.22388."
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"fi": "Aineistossa selvitetään 15-74-vuotiaiden suomalaisten rahapelaamista ja ongelmapelaamisen laajuutta, sekä niihin liittyviä tekijöitä. Kyselyn on toteuttanut Veikkaus Oy pyrkimyksenään tuottaa ajantasaista tietoa rahapelaamisen tuottamien terveyshaittojen laajuudesta, sekä arvioida suunnittelemiensa vastuullisuustoimien vaikuttavuutta. Aluksi kysyttiin, olivatko vastaajat pelanneet elämänsä aikana jotain rahapeliä. Myöntävästi vastanneilta kysyttiin, olivatko he pelanneet jotain rahapeliä viimeisen 12 kuukauden aikana. Tämän jälkeen kartoitettiin, pelattiinko rahapelejä internetissä, ja pelattiinko kotimaisten vai ulkoimaisten palveluntarjoajien pelejä. Lisäksi tiedusteltiin syitä ulkomaisten rahapelien pelaamiseen. Seuraavaksi kysyttiin, kuinka usein erilaisia rahapelejä, kuten nettipokeria, arvontapelejä ja vedonlyöntipelejä pelattiin, ja pyydettiin arvioimaan kuinka paljon rahaa rahapeleihin käytettiin keskimäärin yhden viikon aikana. Kyselyn loppuosa käsitteli vastaajien suhdetta rahapelaamiseen. Kartoitettiin, kuinka usein hävittyjä rahoja yritettiin voittaa takaisin, ja oliko omasta menestyksestä rahapelaamisessa valehdeltu muille ihmisille. Lisäksi kysyttiin, oliko pelejä pelattu enemmän kuin oli alkuperin ollut aikomus, ja oliko pelaaminen aiheuttanut syyllisyydentunteita. Kysyttiin myös, oliko rahapelaamiseen liittynyt lopettamisen vaikeutta, poissaoloja töistä tai opiskelusta, pelaamisen piilottelua läheisiltä, tai kiistelyä rahankäytöstä läheisten kanssa. Rahankäyttöön liittyen kysyttiin myös, oltiinko rahapelien pelaamista varten lainattu rahaa läheisiltä, pankilta tai muilta luottolaitoksilta, myyty omaa tai perheen omaisuutta tai otettu pikavippejä. Lisäksi kysyttiin, koettiinko oma rahapelaaminen ongelmalliseksi, ja oliko kukaan ulkopuolinen puuttunut pelaamiseen. Lopuksi pyydettiin kertomaan, millaisten pelien pelaamisen koettiin aiheuttavan ongelmia pelaamisen hallinnan kanssa. Tutkimuksessa on käytetty Suomen oloihin sovitettua, ongelmapelaamista kuvaavaa South Oaks Gambling Screen (SOGS-R)-mittaria. Taustamuuttujina aineistossa ovat ikäryhmä, sukupuoli, tilastollinen kuntaryhmä, maakunta, suuralue, koulutusaste, pääasiallinen toiminta ja keskimääräiset kuukausitulot."
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