ALBERT

Description: This dataset provides friction data from ring-shear tests on feldspar sand FS900S used for the simulation of brittle behaviour in crust- and lithosphere-scale analogue experiments at the Tectonic Modelling Laboratory of the University of Bern (Zwaan et al. in prep; Richetti et al. in prep). The materials have been characterized by means of internal friction parameters as a remote service by the Helmholtz Laboratory for Tectonic Modelling (HelTec) at the GFZ German Research Centre for Geosciences in Potsdam (Germany). According to our analysis both materials show a Mohr-Coulomb behaviour characterized by a linear failure envelope. Peak, dynamic and reactivation friction coefficients of the feldspar sand are μP = 0.65, μD = 0.57, and μR = 0.62, respectively, and the Cohesion of the feldspar sand is in the order of 5-20 Pa. An insignificant rate-weakening of less than 1% per ten-fold rate change is registered for the feldspar sand. Granular healing is also minor.

Description: This dataset includes five stations of an Ocean Bottom Seismometer (OBS) experiment conducted at the southern end of the Fonualei Rift and Spreading Center in the Lau Basin, southwestern Pacific. The OBS recorded continuously for 32-days on 4 components, including a hydrophone and a 3-component 4.5 Hz geophone. The experiment was conducted during RV Sonne cruise SO267, project ARCHIMEDES I. In the article, the authors report an increasing trend of methane emissions for June and July at a permafrost site in Siberia (Lena River Delta). Using the longest set of observational methane flux data in the Arctic, the authors demonstrate that the continuous warming has begun to trigger the projected enhancement of methane release in Arctic permafrost ecosystems. This software is written in MATLAB. Running the codes ([.m files](Code)) and loading the data files ([.mat files](Data)) requires the pre-installation of [MATLAB](/https://de.mathworks.com/products/matlab.html). IMPORTANT: The repository only contains dummy data. The data that is needed to run the code can be requested by Torsten Sachs and Christian Wille (contact authors). Although the scripts and the data files have been tested for newer versions of MATLAB (〉= MATLAB R2017a). The code might also run in older versions of MATLAB, but this has not been tested.

Description: Orbital products describe positions and velocities of satellites, be it the Global Navigation Satellite System (GNSS) satellites or Low Earth Orbiter (LEO) satellites. These orbital products can be divided into the fastest available ones, the Near Realtime Orbits (NRT), which are mostly available within 15 to 60 minutes delay, followed by Rapid Science Orbit (RSO) products with a latency of two days and finally the Precise Science Orbit (PSO) which, with a latency of up to a few weeks, are the most delayed. The absolute positional accuracy increases with the time delay. This dataset compiles the RSO products for various LEO missions and the appropriate GNSS constellation in sp3 format. The individual solutions for each satellite mission are published with individual DOI as part of this compilation. GNSS Constellation: • GNSS 24h (v01) • GNSS 30h (v02) LEO Satellites: • CHAMP • GRACE • GRACE-FO • SAC-C • TanDEM-X/ TerraSAR-X Each solution is given in the Conventional Terrestrial Reference System (CTS). • The GNSS RSOs are 30-hour long arcs starting at 21:00 the day before the actual day and ending at 03:00 the day after. The accuracy of the GPS RSO sizes at the 3-cm level in terms of RMS values of residuals after Helmert transformation onto IGS combined orbit solutions (Version 1 GNSS RSOs are 24-hour long arcs starting at 00:00 and ending at 24:00 the actual day). • The LEO RSOs are generated based on these 30-hour GNSS RSOs in two pieces for the actual day with arc lengths of 14 hours and overlaps of 2 hours. One starting at 22:00 and ending at 12:00, one starting at 10:00 and ending at 24:00. The accuracy of the LEO RSOs is at the level of 1-2 cm in terms of SLR validation. The exact time covered by an arc is defined in the header of the files and indicated as well as in the filename. This dataset compiles RSO products for various LEO missions and the corresponding GNSS constellation in sp3 format in a revised processing version 2. The switch from previous version 1 to 2 was performed on 18-Feb-2019. Major changes from version 1 to 2 are the change from IERS 2003 to IERS 2010 conventions and ITRF 2008 to ITRF-2014, as well as the temporal extension of the GNSS constellation from previous 24 hours (version 1) to 30 hours (version 2) arcs. This temporal expansion eliminates the chaining of two consecutive 24-hour GNSS constellation solutions previously used to process day-overlapping LEO arcs in Version 1. This 24h GNSS constellation (Version 1) will continue to operate and be stored on the ISDC ftp server, as discussed in more detail in Section 8.1. All RSO LEO arcs will no longer be continued in version 1 after the changeover date and will only be available in version 2 since then.

Description: To address climate change, the decarbonisation of Germany's existing building stock urgently needs to be prioritised. However, the rate and depth of refurbishment has lagged behind official targets for years. This is a particular problem in the rental sector, where the costs and benefits of energy efficiency measures tend to be unevenly distributed between landlords and tenants (the so-called "landlord-tenant dilemma"). Within the context of the current policy landscape, investments in energy efficiency consequently make most sense for landlords if the upfront costs can be refinanced via increased rental income or reduced vacant periods. This paper seeks to investigate the validity of this statement at city level by using a large dataset from one of Germany’s main internet property platforms to examine how the willingness of tenants to pay for energy efficiency varies across residential locations in the city of Wuppertal. The small-scale spatial analysis highlights the existence of a price premium for energy efficiency in the rental market for apartments; however, this premium is generally small (especially in comparison to other property enhancements, especially visible improvements) or even non-existent in some residential areas. Consequently, investing in energy efficiency is rarely an attractive option for landlords. Therefore, strong policy action, aligned with social and urban development policy objectives, is necessary to establish an effective incentive structure in the market and make investing in energy efficiency more attractive for both landlords and tenants.