This data publication contains maps resulting from spatial prioritisations conducted for the iAtlantic D5.3 report on Systematic Conservation Planning of the wider Atlantic Ocean based on results generated by the iAtlantic project. The maps were produced using the prioritizr R package (Hanson et al. 2023), which identifies priority areas for achieving specific conservation goals while minimising costs. The various prioritisations were developed to address multiple research questions related to: (1) identifying priority areas for conservation and restoration, (2) transboundary conservation, (3) climate-smart conservation planning, and (4) protecting 30% of the Atlantic Ocean, including 10% under strict protection. The results are organised into subfolders based on the research questions addressed and further categorised into data-rich and data-poor regions, along with aggregate results for each region. Further, the results are organised into subfolders representing multiple scenarios executed using various cost layers, including area-based, Global Fishing Watch (GFW, 2023) benthic, GFW total fishing, Global Fisheries Landings (GFL, Watson 2019) v4.0 benthic, and GFL v4.0 total landings. Each map filename provides descriptive information about the executed scenario.
Subterrane Ökosysteme beherbergen eine breite Vielfalt spezialisierter und endemischer Organismen, die einen einzigartigen Bruchteil der globalen Vielfalt ausmachen. Darüber hinaus leisten sie entscheidende Beiträge der Natur für die Menschen – insbesondere die Bereitstellung von Trinkwasser für mehr als die Hälfte der Weltbevölkerung. Diese unsichtbaren Ökosysteme werden jedoch bei den Biodiversitäts- und Klimaschutzzielen für die Zeit nach 2020 übersehen. Nur 6,9 % der bekannten subterranen Ökosysteme überschneiden sich mit dem ´Netzwerk von Schutzgebieten. Zwei Haupthindernisse sind für diesen Mangel an Schutz verantwortlich. Erstens bleiben subterrane Biodiversitätsmuster weitgehend unkartiert. Zweitens fehlt uns ein mechanistisches Verständnis der Reaktion subterraner Arten auf vom Menschen verursachte Störungen. Das DarCo-Projekt zielt darauf ab, subterrane Biodiversität in ganz Europa zu kartieren und einen expliziten Plan zur Einbeziehung subterraner Ökosysteme in die Biodiversitätsstrategie der Europäischen Union (EU) für 2030 zu entwickeln. Zu diesem Zweck haben wir ein multidisziplinäres Team führender Wissenschaftler in subterraner Biologie und Makroökologie zusammengestellt und Naturschutz aus einem breiten Spektrum europäischer Länder. Das Projekt gliedert sich in drei Arbeitspakete, die der direkten Forschung gewidmet sind (WP2-4), plus ein viertes (WP5), das darauf abzielt, die Verbreitung der Ergebnisse und das Engagement der Interessengruppen für die praktische Umsetzung des Naturschutzes zu maximieren. Zunächst werden wir durch die Zusammenstellung bestehender Datenbanken und die Nutzung eines kapillaren Netzwerks internationaler Mitarbeiter Verbreitungsdaten, Merkmale und Phylogenien für alle wichtigen subterranen Tiergruppen sammeln, einschließlich Krebstiere, Mollusken, Insekten und Wirbeltiere (WP2). Diese Daten werden dazu dienen, die Reaktionen von Arten auf menschliche Bedrohungen mithilfe der hierarchischen Modellierung von Artengemeinschaften (WP3) vorherzusagen. Die Vorhersagen der Modelle zur Veränderung der biologischen Vielfalt werden die Grundlage für eine erste dynamische Kartierung des subterranen Lebens in Europa bilden. Durch die Verschneidung von Karten von Diversitätsmustern, Bedrohungen und Schutzgebieten werden wir einen Plan zum Schutz der subterranen Biodiversität entwerfen, der das aktuelle EU-Netzwerk von Schutzgebieten (Natura 2000) ergänzt und gleichzeitig klimabedingte Veränderungen in subterranen Ökoregionen berücksichtigt (WP4). Schließlich versuchen wir durch gezielte Aktivitäten in WP5, das gesellschaftliche Bewusstsein für subterrane Ökosysteme zu schärfen und Interessengruppen einzuladen, die subterrane Biodiversität in multilaterale Vereinbarungen einzubeziehen. In Übereinstimmung mit dem europäischen Plan S werden wir alle Daten offen und wiederverwendbar machen, indem wir eine zentralisierte und offene Datenbank zum subterranen Leben entwickeln – die Subterranean Biodiversity Platform.
Here, we examine the ecosystem ramifications of changes in sediment-dwelling invertebrate bioturbation behaviour—a key process mediating nutrient cycling—associated with nearfuture environmental conditions (+ 1.5 °C, 550 ppm [pCO2]) for species from polar regions experiencing rapid rates of climate change. This dataset is included in the OA-ICC data compilation maintained in the framework of the IAEA Ocean Acidification International Coordination Centre (see https://oa-icc.ipsl.fr). Original data were downloaded from Polar Data Centre (see Source) by the OA-ICC data curator. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2024) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2024-07-11.
<p>Im Pilotprojekt <a href="https://www.ki-ideenwerkstatt.de/unterstuetzung-materialien/pilotprojekte/toxfox-mithilfe-von-ki-inhaltsstoffe-scannen/"><em>ToxFox</em></a> wurde 2024/25 gemeinsam mit dem <a href="https://www.bund.net/">BUND</a> eine KI-basierte OCR-Lösung (englisch: optical character recognition, Bild zu Text) entwickelt. Hierbei wird via FastAPI-Webanwendung ein per Smartphonekamera aufgenommens Bild der Informationstextes der Inhaltsstoffe auf Verpackung eines Produktes erfasst und anschließend eine Liste der gefundenen Schadstoffe nach INCI zurückgegeben. INCI steht für die Internationale Nomenklatur für kosmetische Inhaltsstoffe. Bisher musste für diesen Prozess der Barcode der Produkte gescannt werden. Mit der OCR-Lösung sollen u.a. Fehler korrigiert werden, die durch veraltete, auf Barcodes enthaltene Informationen entstehen oder sich durch eine Umbenennung der Chemikalien durch die Industrie ergeben könnten.</p>
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 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 dataset provides carbonate chemistry and hydrological measurements supporting the analysis of the stability of alkalinity and carbon transport potential in the Elbe Estuary, northern Germany. It includes (1) results from laboratory incubation experiments using water samples from the Elbe conducted in 2023, (2) historical water chemistry monitoring records from multiple stations, and (3) monthly flow discharge measurements from the Neu Darchau gauging station. Experimental data were collected from the experiments varying salinity and seasonal conditions, and parameters measured include pH, temperature, and total alkalinity. Major ion concentrations (Na+, K+, Ca2+, Mg2+, Cl-, SO42-) were reconstructed from stoichiometry. The saturation states of calcite and aragonite, as well as pCO2, were calculated using the phreeqpython geochemical package. Historical data, covering carbonate chemistry and major ions at several stations and over multiple years, were collected from digitized sources and FGG Elbe. Together, this dataset facilitates the investigation of long-term trends in the carbonate system and carbon transport in the land ocean transition zone of the Elbe River.
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
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