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Please contact Maxime Hervo from MeteoSwiss for information about the data set.

Project: IPCC Data Distribution Centre : Assessment Report Five data sets - The Intergovernmental Panel on Climate Change (IPCC) has been established by WMO and UNEP to assess scientific, technical and socio-economic information, relevant for the understanding of climate change, its potential impacts and option for adaption and migration. Projection of future trends for a number of key variables are provided through this section of the DDC (http://www.ipcc-data.org/sim/gcm_monthly/AR5/ ). Based on the interests and needs of model developers and data users, global scenarios for two time periods where developed: - near-term scenarios that cover the period to about 2035; and - long-term scenarios that cover the period to 2100 and, in a more stylized way, the period to 2300. Four RCPs (Representative Concentration Pathways: RCP2.6, RCP4.5, RCP6.0 and RCP8.5) were constructed from long-term scenarios available in the published literature. These scenarios include time paths for emissions and concentrations of the full suite of greenhouse gases and aerosols and chemically active gases, as well as land use/land cover. They describe alternative technological, socioeconomic, and policy futures including both reference (without explicit climate policy intervention) and climate policy scenarios. The data represent a subset of data sets from the distributed CMIP5 (Coupled Model Intercomparison Project Phase 5) data archive: http://cmip-pcmdi.llnl.gov/cmip5, which was collected by the ETH Zurich in support of the IPCC WG 1 authors. Data are available in NetCDF/CF format (http://cf-pcmdi.llnl.gov/). Fifth Assessment Report of the IPCC: IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp, doi:10.1017/CBO9781107415324. Summary: historical is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( http://cmip-pcmdi.llnl.gov/cmip5/ ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the IPCC AR5. This subset of the CMIP5 additional historical data were collected by ETH Zurich until 15th March 2013 in support of IPCC AR5 Working Group 1 ( http://wiki.c2sm.ethz.ch/COSMO/CMIP5 ). These data are part of the IPCC-DDC AR5. historical (3.2 Historical) - Version 2: Simulation of recent past (1850 to 2005). Impose changing conditions (consistent with observations). Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Data are structured and entry names are set according to the ETH Zurich Archive layout: "experiment/MIP table/variable/model/ensemble member/CMOR filename.nc"

XRF (Elio: 40kV, 80 µA, spot measurements of 90s each) and reflectography (DinoLite: x50 magnification, vis, NIR and UV light) analysis of inks from Vatican Library Ms Reg. Gr. 107. The Organon manuscript Città del Vaticano, Biblioteca Apostolica Vaticana, Reg. Gr. 107 is a paper, composite manuscript consisting of two codicological units. The oldest unit was written around 1330-1350 by an anonymous scribe. The youngest unit was written around 1380-1390 by a team of scholars. The youngest unit (ff. 1-95) was produced to complement the defective oldest unit (ff. 97-487). Moreover, the scholars who made the youngest unit added a dense exegetical paracontent on the folios of the oldest unit. By the end of the 15th and beginning of the 16th century, Reg. Gr. 107 was read and annotated by three users, one of whom was identified as Demetrios Chalkondyles. Reg.gr.107_protocol.pdf - protocol RegGr107_XRF.zip - complete XRF dataset Report_RegGr107.docx - detailed report VatReg107_reflectography - complete reflectography dataset The research for projects RFD07 and RFK02 was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2176 'Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures', project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universität Hamburg.

Files 1-3: RTI interactive photographs of the skulls of the drepanosauromorphs Megalancosaurus preonensis Calzavara, Muscio and Wild, 1980 and Vallesaurus cenensis Renesto and Binelli, 2006 (Norian, Italy). Reflectance Transformation Imaging (RTI) is a method that computes an 'interactive specimen' on which the illumination can be oriented at will. Sets of 54 photographs under different LED sources were compiled using the RTIBuilder software. The RTI files can be opened using the RTI vewer software (both softwares are freely available at www.culturalheritageimaging.org). Files 4-6: Nexus files of phylogenetic taxon-character matrices and backbone constraints used in phylogenetic analyses. Currently only available upon request for reviewers, but will be made public after acceptance of article for publication.

In this work, we present Python code to run the FDTD simulations, as well as the OpenFOAM cases (and the custom OpenFOAM solver code required by that case), that were used to prepare the figures in the manuscript with the title "Multi-scale Dynamics of Ultrafast Laser Nano-Ablation of Water" by J. Hernandez-Rueda and D. van Oosten

In-session dropout prediction model This project describes an in-session prediction model that predicts student early dropout from online learning exercises. Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates, school homework and assignments are supposed to be completed by all learners. In the pandemic, online learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the online language learning platform orthografietrainer.net. We calculated time-progressive machine learning models that predict dropout after each step (completed sentence) in the assignment using learning process data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention strategy that distinguishes between motivational and subject-specific interventions. A random state is not set, thus, results might differ marginally. Whole project described in: N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, SciTePress, 2022, ISBN 978-989-758-562-3 {"references": ["N. Rzepka, K. Simbeck, H.-G. M\u00fcller, and N. Pinkwart Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, SciTePress, 2022, ISBN 978-989-758-562-3"]}

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: 'sstClim4xco2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (http://cmip-pcmdi.llnl.gov/cmip5/). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 6.2b sstClim4xco2 (6.2b SST Climatology With 4XCO2 Forcing) - Version 1: AMIP-style experiment with control run climatological SSTs and sea ice (as in 6.2a) but with quadrupled 4XCO2 imposed. Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: 'decadal1997' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( http://cmip-pcmdi.llnl.gov/cmip5/ ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. decadal1997 (10-year hindcast/prediction initialized in year 1997) - Version 2: The atmospheric composition (and other conditions) should be prescribed as in the historical run (expt. 3.2) and the RCP4.5 scenario (expt. 4.1) of the long-term suite of experiments. Ocean initial conditions should be in some way representative of the observed anomalies or full fields for the start date. Land, sea-ice and atmosphere initial conditions are left to the discretion of each group. Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax ( http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .