This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
A fossil pollen dataset distributed across Europe (10° W - 43° E, 33° - 71° N) comprising 520 records was extracted from the LegacyPollen 1.0 database (Herzschuh et al., 2022) to reconstruct climatic variables including Annual temperature (TANN), Annual precipitation (PANN), Winter Temperature (December, January, February; TDJF), Summer Temperature (June, July, August; TJJA). Short records not reaching beyond 1 ka BP were also excluded to keep the dataset refined, as the syntheses aim to cover the entire Holocene (i.e., 11-1 ka BP). The modern pollen training dataset was integrated from Legacy Climate 1.0 (Herzschuh et al., 2023) and the EMPD2 (Davis et al., 2020). Two different approaches were applied in parallel to reconstruct climate variables from fossil pollen assemblages, namely Modern Analogue Technique (MAT) and Weighted Averaging Partial Least Squares (WAPLS). Reconstruction uncertainties were provided as Root Mean Squared Errors of Prediction (RMSEPs). All the reconstructions and tests were conducted using the rioja and analogue packages in R (R Core Team, 2019). The synthesized results were interpolated from all reconstructed climate records. The mean value of reconstructed climatic variables with the same ages was calculated before any interpolations. Due to the different chronological resolution of the time series, the sequences were then interpolated to equidistant time series of 50-year intervals. Two different interpolation methods were applied in R. The first is to use the interp.dataset function from rioja package with loess regression to interpolate the dataset as a whole. The second is to interpolate each complete record that can cover the Holocene (i.e., 11-1 ka) and has a mean resolution of less than 1ka separately using the corit package with linear regression and then calculate the mean of these records. To perform the latter interpolation, a total of 214 records covering the entire period between 11-1 ka BP were used. The Root Mean Squared Errors (RMSEs) were calculated for the synthesis results.
This Python package is a collaborative effort by the gravity Metrology group at the German Federal Agency for Carthography and Geoesy (BKG) and the Hydrology section at GFZ Helmholtz Centre for Geosciences. It comprises functionalities and features around the respectively new instrument type of a Quantum Gravimeter (here AQG). New (standardized) instrument data format additional to new measurement and processing concepts lead to the first collection of scripts and now complete python package for a fully-featured analysis of AQG data. This encompasses live-monitoring while the instrument is actually measuring (with enhanced functionality than what is provided by the manufacturer), data processing, visualizations as well as archiving data, fulfilling the idea of reproducible data within FAIR principles. Many of these functionalities and concepts also apply to other gravimeter types. It is thus planned to include also access and processing of data for these other devices (starting in the near future with CG-6 relative gravimeters). This package is actively maintained and developed. If you are interested in contributing, please do not hesitate to contact us. Please find instructions for its installation and usage in the documentation or git repository, linked in the left panel. gravitools is listed in the python standard repository database "PyPi". Some highlight features, available in the first official stable release are: • Read and process raw data of the Exail Absolute Quantum Gravimeter (AQG) • Apply standardized or customized AQG data processing and outlier detection • Read and write processed datasets with metadata to .nc-files in NETCDF4-format • Handle Earth orientation parameters (EOP) from iers.org for polar motion correction • Visualize data with matplotlib • CLI for standard processing of AQG raw data to .nc-file • Dashboard for real-time processing and visualization during measurements (on AQG laptop) • Dashboard includes a proposed standard template for a measurement protocol • Standardized, easy-to-read and modify config files for processing options and reproducible data handling • Generation of PDF reports from individual measurements
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).
This data set presents the reconstructed vegetation cover for 3083 sites based on harmonized pollen data from the data set LegacyPollen 2.0 (https://doi.pangaea.de/10.1594/PANGAEA.965907) and optimized RPP values. 1115 sites are located in North America, 1435 in Europe, and 533 in Asia. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Townshend 2016) for North America, Europe, and Asia. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for all Europe, North America and Asia. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.
This data set presents the reconstructed vegetation cover for 1451 European sites based on harmonized pollen data from the data set LegacyPollen 2.0 and optimized RPP values. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal and forest cover was reconstructed. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Sexton et al. 2013) for Europe. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for all records in Europe. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.
This data set presents the reconstructed vegetation cover for 706 Asian sites based on harmonized pollen data from the data set LegacyPollen 2.0 and optimized RPP values. Sugita's REVEALS model (2007) was applied to all pollen records using REVEALSinR from the DISQOVER package (Theuerkauf et al. 2016). Pollen counts were translated into vegetation cover by taking into account taxon-specific pollen productivity and fall speed. Additionally, relevant source areas of pollen were also calculated using the aforementioned taxon-specific parameters and a gaussian plume model for deposition and dispersal and forest cover was reconstructed. In this optimized reconstruction, relative pollen productivity estimates for the ten most common taxa were first optimized by using reconstructed tree cover from modern pollen samples and LANDSAT remotely sensed tree cover (Sexton et al. 2013) for Asia. Values for non-optimized taxa for relative pollen productivity and fall speed were taken from the synthesis from Wiezcorek and Herzschuh (2020). The average values from all Northern Hemisphere values were used where taxon-specific continental values were not available. We present tables with optimized reconstructed vegetation cover for records in Asia. As further details we list a table with the taxon-specific parameters used and a list of parameters adjusted in the default version of REVEALSinR.
The NEARESTproject (Integrated observations from NEAR shore sourcES of Tsunamis: towards an early warning system) aimed at the identification and characterization of potential near-shore sources of tsunamis in the Gulf of Cadiz. This area is well known from the catastrophic earthquake and tsunami that destroyed Lisbon and several other places mainly along the EastAtlantic coast on November 1st, 1755. One of the project's work packages dealed with monitoring of recent seismic activity in the Gulf of Cadiz area. For this purpose 24 broadband ocean-bottom seismometers (OBS) from the German DEPAS instrument pool were deployed for 11 months in addition to the GEOSTAR multi-parameter deep-sea observatory and two temporary land stations in Portugal. The GEOSTAR observatory and the 24 OBS were deployed and recovered during two expeditions with RV Urania in 2007 and 2008. The OBSs consist of three‐component Guralp CMG‐40T‐OBS seismometers and HighTech HTI‐04‐PCA/ULF hydrophones. A wide range of signals was recorded, ncluding teleseismic, regional and local earthquakes, and low‐frequency (∼20 Hz) vocalization of fin whales. The GEOSTAR observatory was again deployed between 2009 and 2011. The Portuguese temporary land station PDRG was additionally recording during the NEAREST project. Originally, the position of recovery on deck was taken to calculate the mean coordinate of the OBS at depth from deployment and recovery coordinates. In most cases the difference in coordinates between deployment and recovery is very small (table 3 and 4 in Carrara et al., 2008). For two stations, the location at the seafloor could be measured by triangulation (Carrara et al., 2008). Due to experience of other experiments over the years, we finally suggest to use the deployment coordinates as the station coordinates for all stations that could not be tri-angulated. The clocks were synchronized with GPS time before the deployment and if possible again after the recovery. Unfortunately, most of the batteries were empty at the end of the recording period. That either made it impossible to realize the second synchronisation (skew time measurement) or in some case also caused erroneous synchronisations. Therefore, the internal clock drift was estimated by ambient noise analysis (Corela, 2014). The internal clock drifts were corrected using a linear interpolation method. Generally, the data quality is very good, especially for the intended study of local and regional earthquakes. Studies relying on wideband seismological recordings can also be carried out. The sensor package and noise conditions hamper the use for broadband and very broadband applications. Unfortunately, also not all channels operated properly, therefore hampering the use of multi-component methods for the relevant stations. We thank the captain E. Gentile, crew, G. Carrara, and all participants of the R/V URANIA expeditions in 2007 and 2008. We are grateful to all people and institutions involved in the NEAREST project. Waveform data is available from the GEOFON data centre, under network code 9H.
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