Organic carbon (OC) content, nitrogen content, C/N ratio, carbonate content, δ13C of OC, mean grain size, surface area, calculated fraction, content and loading of terrestrial and marine OC in surface sediments of Helgoland Mud Area in the North Sea

The grain size of sediments was determined with a Cilas 1180 laser-diffraction particle analyzer (range 0.04–2500 μm) and the mean grain size was calculated. The surface area (SA) was calculated from the grain size distribution of the sediments. The OC content of surface sediments was determined using a carbon-sulfur analyzer (CS-125, Leco) after decarbonization with HCl. The total carbon (TC) and nitrogen (TN) contents of the samples were analyzed using a carbon-nitrogen-sulfur analyzer (Elemental III, Vario) and used to calculate carbonate contents (CaCO3= (TC − OC) × 8.333) and the mass ratio of OC to TON content ((C/N)OC), which was corrected for mineral-associated inorganic N. The stable carbon isotope composition of OC in surface sediments (δ13COC) was measured using a Thermo Delta isotope ratio mass spectrometer coupled to a Carlo Erba elemental analyzer. δ13COC values are given in per mil notation relative to the Vienna Pee Dee Belemnite standard. The standard deviation of duplicate analyses ranged from 0.01‰ to 0.18‰, with an average of 0.10‰. See details in related publication (Wei et al., 2025)

Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2019T58

The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2019T58 (a.k.a. FMI05-09) is an autonomous instrument that was installed on drifting sea ice in the Central Arctic Ocean (Polarstern PS122 (MOSAiC) in 2019/20) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 241 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2019-10-08T01:00:14 and 2020-07-08T07:00:14. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt

Larval size of Strongylocentrotus purpuratus during laboratory experiments with different food treatments

Strongylocentrotus purpuratus (Stimpson 1857) originally collected in November 2022 from La Jolla, USA (Lat: 32.842674; Long: -117.257767) were held in flow-through tanks which were filled with water from Kiel Fjord adjusted to 31.5 psu in the Christian-Albrechts-Universität zu Kiel at 10 °C. For experimental characterization of carbohydrate digestion between September and November 2023, S. purpuratus larval cultures were maintained with two different food conditions (low food vs. high food) under low light conditions, following a 12-hour dark to 12-hour light cycle at 15°C, with continuous mixing facilitated by a gentle stream of pressurized air bubbles. Health assessments and larval size were conducted by sampling two 10 ml water samples at 0, 3, 5, 7, 9 & 10 days larval age

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Fulda, Germany from 1984 – 2023

The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research

Data on nematode genus abundance and counts at LTER HAUSGARTEN from 2000 to 2009, 2010, 2014 and 2019

This dataset contains information on Arctic deep-sea nematode abundance calculated for 10 cm² of seafloor on genus level (time period 2000 to 2009), as well as counts (years 2010, 2014 and 2019). Sampling was carried out in the Arctic summer with a multiple corer (MUC) at the LTER HAUSGARTEN observatory at three stations in 2000 to 2009, 2014 and 2019 (HG-I, HG-IV, HG-VII), and at nine stations in 2010 (HG-I to HG-IX). From each MUC deployment, three replicate samples were taken from different MUC cores (10 cm diameter), and the top five centimetres of sediment from each replicate sample were subsampled with a syringe (2.2 cm diameter). All samples were fixed in formol (4 %) and rinsed over a 32 µm sieve. The metazoan meiofauna was extracted by centrifugation in colloidal silica. Morphological genus determination, body size measurements and image acquisition of the nematodes were performed under a light microscope equipped with a digital camera and associated software

Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2016T42

The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2016T42 (a.k.a. Awi_88) is an autonomous instrument that was installed on drifting sea ice in the Antarctic Ocean (Polarstern PS96 (ANT31/2, FROSN) in 2015/16) as part of the project Advanced Remote Sensing – Ground-Truth Demo and Test Facilities (ACROSS), Sea Ice Physics @ AWI (AWI_SeaIce). Its thermistor chain is 5 m long, and equipped with 240 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2016-01-18T14:00:39 and 2016-12-23T08:00:39. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt

Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2014T33

The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2014T33 (a.k.a. FMI_15) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Polarstern PS87 (ARK28/4, ALEX) in 2014) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 240 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2014-08-27T08:00:39 and 2015-04-08T08:00:39. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt. Due to a malfunction of the on-board GPS unit on 02 November 2014 (12 UTC), all position values after that date are based on cleaned and smoothed (3-day running mean) position readings derived from the Iridium satellite network system

Raw physical properties measured on sediment core GeoB19904-2

Sound knowledge about the petrophysical characteristics of a sediment core is a prerequisite for any subsequent paleoenvironmental study relying on sediment proxies. Physical properties of whole-round sediment cores are acquired as a first step of sediment core description, prior to slicing of sediment cores into two halves. Here, we present raw physical-properties data of core GeoB19904-2 acquired with a Geotek MSCL-S system on the unsplit core, retrieved during RV Maria S. Merian expedition MSM44 to West Greenland/Baffin Bay in summer 2015

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Karlsruhe, Germany from 1984 – 2023

The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research