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Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011). Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).

A global compilation of in situ data is vital to evaluate the quality of ocean-colour satellite data records. Here, we describe data compiled for the validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO) and span the period from 1997 to 2021. Observations of the following variables were compiled: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. The data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The result is a merged table available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version.

Individual PCB congener mass per PUF from 24 laboratory blanks: PCBs were analyzed using GC-MS/MS.

The type of datum:yearofcollection = the year that the core was collected, which is used to constrain the core top age.uth: U/Th date on primary aragonite, from Torfstein et al. (2015).c14: radiocarbon date on terrestrial plant debris, from Kitagawa et al. (2017).tiepointG: tiepoint to dated outcrops of the Lisan Formation, from Goldstein et al., (2020).tiepointS: tiepoint to the Soreq Cave speleothem oxygen isotope record, from Goldstein et al., (2020).tiepointO: tiepoint to Northern Hemisphere summer insolation, from Goldstein et al., (2020). The age model for this core was updated to the IntCal20 curve (for radiocarbon dates) using BACON age modeling software (https://cran.r-project.org/web/packages/rbacon/). The data span 160,000 years and describe changes in Dead Sea hydroclimate and the surrounding landscape.

Sediment cores at sites J1002 awere collected from the Chilean Margin during D/V JOIDES Resolution Expedition 379T (JR100) in 2019. Sedimentary U/Th derived from natural gamma radiation data were collected during the expedition. U/Th was further used as a non-quantitative indicator of redox conditions of the sediments. Further funding: NETL Methane Hydrates Fellowship Program

Atmospheric abundances of Nitrogen dioxide (NO2), Nitrogen oxide (NOx), Nitric oxide (NO) and Ozone (O3) have been measured at Antarctic station Dome C (-75.10 lat., 123.33 long., 3233 m a.s.l). They have been retrieved from direct and in-situ atmospheric measurements using newly developed optical instruments based on absorption spectroscopy (incoherent broadband cavity enhanced absorption spectroscopy or IBBCEAS). These instruments directly measure NO2 with a detection limit of 30 pptv (parts per trillion by volume or 1E-12 mol/mol) (3σ). We performed two sets of measurements in December 2019 (4th to 9th) and January 2020 (16th to 25th) to capture the early and late photolytic season, respectively. The dataset also contains photolysis rate coefficients of Nitrogen dioxide and Ozone, and the Ozone production rate. Calculation of the O3 production rate P_{O3}:P_{O3} = (k_{RO2+NO}[RO2] + k_{HO2+NO}[HO2]) * [NO] * Vwith k_{RO2+NO} and k_{HO2+NO} the kinetic rate coefficients of the reactions RO2 + NO → NO2 + RO and HO2 + NO → NO2 + OH, respectively, expressed in cm**3/molecules/s; [RO2] and [HO2], the species concentrations expressed in molecule/cm**3 and derived from the correlation between J(NO2) and [RO2] and the [RO2]/[HO2] ratio (Kukui et al., 2014); [NO] the concentration of NO expressed in molecule/cm**3; and V the arbitrary volume expressed as 1 cm * 1cm * H_PBL cm with polar boundary layer height H_PBL, given by the MAR regional model.

Dataset contains all species-specific, genus-specific and mixed-taxa foraminifer-bound FB-δ15N and FB-N content measurements from ODP 1209.

The data file contains information on each sample (Site, core, depth, age) and the measurements (replicate number, laboratory) in addition to the isotope data. For clumped isotopes (D47), mean values and standard errors are given (on the I-CDES scale, see Bernasconi et al., G3, 2021) as well as temperatures calculated using the foraminifera-based calibration of Meinicke et al. (GCA, 2020), updated to the I-CDES scale by Meinicke et al. (Paleoceanography and Paleoclimatology, 2021). Furthermore, genus-specific d18O and d13C values are reported for Cibicidoides and Nuttalides where available, as well as the calculated isotopic composition of seawater based on the d18O values from Cibicidoides spp., the D47 temperatures, and the calibration of Marchitto et al. (GCA, 2014). d18O of Cibicidoides and resulting seawater d18O are also reported after correction for a hypothetical pH effect using a linear trend through reconstructed deep ocean pH based on d11B and the theoretical pH effect of 1.42 ‰ per pH unit from Zeebe (Paleo3, 2001). This dataset contains clumped isotope (D47), d18O and d13C data from benthic foraminifera from four IODP sites from the Newfoundland margin. The D47 data were used to reconstruct deep ocean temperature across the Cenozoic era. The reported data were generated at ETH Zürich and the University of Bergen between 2015 and 2020. Data for this study were mostly obtained from core catcher samples, with an average time resolution of 1.2 million years. For each sample, 13-45 replicate measurements were performed on different species of benthic foraminifera. Data in this dataset are sample-averaged isotope and temperature data. In addition, replicate-level raw data including standard data for correction are stored at Earthchem (doi:10.26022/IEDA/112213) to allow for reprocessing of the data.