dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). It is possible that some of the differences arise from the use of different methodologies (instruments, time scale, global representation of the dataset) to address the complexity of the processes to be measured simultaneously. Such opposite pattern between A_Zoo and B_zoo in one hand and A_Flux in the other hand supports the view that the abundant zooplankton community plays a crucial role in flux attenuation in productive layers, by feeding and fragmenting the sinking material as suggested in previous studies  (Dilling and Alldredge, 2000; Lampitt et al. , 1990; Sarah L. C. Giering et al. , 2014; Stemmann et al. , 2004). However, on average and more importantly at high latitudes, zooplankton attenuation rates were stronger than the vertical flux attenuation rates (Fig. 3 ) indicating that zooplankton vertical zonation may be affected by other processes than the resources provided by the flux of organic matter from the epipelagic layer.
ACKNOWLEDGMENTS Vertical particle mass fluxes (mg Dry Weight m -2 d -1 ) were calculated from the particle size spectra (150 µm -1. 5 mm) detected by the UVP5 following  Guidi et al. , (2008). Briefly, the procedure optimized two parameters in the flux equation by comparing UVP5 derived flux and a global sediment trap flux data set  (Guidi et al. , 2008). Depending on the ocean basin, regional algorithms with different sets of the two parameters have been proposed  (Forest et al. , 2013; Kiko et al. , 2017)  but the shape of the allometric relationships between particle size and flux is kept as a growing monotonic power relationship. Many studies have shown that this assumption is verified when comparing extremes size of particles but they all have also shown a high variability  (Guidi et al.
Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Ocean Foundation and the continuous support of 23 institutes (https://oceans. taraexpeditions. org/). This study is part of the "Ocean Plankton, Climate and Development" project funded by the French Facility for Global Environment (FFEM). Y. D. S. and M. C. B. received financial support from FFEM to execute the project. R. K acknowledges support support via a "Make Our Planet Great Again" grant of the French National Research Agency within the "Programme d'Investissements d'Avenir"; reference "ANR-19-MPGA-0012". F. B. received support from ETH Zürich and the European Union's Horizon 2020 research and innovation programme under grant agreement n°SEP-210591007.
We further thank the commitment of the following sponsors: CNRS (in particular Groupement de Recherche GDR3280 and the Research Federation for the Study of Global Ocean Systems Ecology and Evolution FR2022/Tara Oceans-GOSEE), the European Molecular Biology Laboratory (EMBL), the French Ministry of Research, and the French Government ''Investissements d'Avenir'' programs OCEANOMICS (ANR-11-BTBR-0008), the EMBRC-France (ANR-10-INBS-02). Funding for the collection and processing of the Tara Oceans data set was provided by NASA Ocean Biology and Biogeochemistry Program under grants NNX11AQ14G, NNX09AU43G, NNX13AE58G, and NNX15AC08G (to the University of Maine); the Canada Excellence research chair on remote sensing of Canada's new Arctic frontier; and the Canada Foundation for Innovation. We also thank Agnès b.
Availability of the dataset Data sets for the environment are available at Pangea: (https://doi. org/10. 1594/PANGAEA. 840721) Tables for the RDA are available For zooplankton: Metadata_Zooplankton_Article_RDA. xls Zooplankton Pangea for the tables/ecotaxa for the vignettes https://ecotaxa. obsvlfr. fr/prj/714?taxo=56693&taxochild=N&ipp=100&zoom=100&sortby=&magena bled=0&popupenabled=0&statusfilter=&samples=&instrum=&sortorder=asc&disp field=&projid=714&pageoffset=0 Quality of the dataset The analysis reported here is the first to apply consistent imaging techniques in order to investigate particle flux, zooplankton community composition and biomass across the global ocean. To do so, our study relies on the combined use of the Underwater Video Profiler v5 (UVP5), a Multinet and the Zooscan imaging system.
Tara Oceans (which includes both the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Ocean Foundation and the continuous support of 23 institutes (https://oceans. taraexpeditions. org/). This study is part of the "Ocean Plankton, Climate and Development" project funded by the French Facility for Global Environment (FFEM). Y. D. S. and M. C. B. received financial support from FFEM to execute the project. R. K acknowledges support support via a "Make Our Planet Great Again" grant of the French National Research Agency within the "Programme d'Investissements d'Avenir"; reference "ANR-19-MPGA-0012". F. B. received support from ETH Zürich and the European Union's Horizon 2020 research and innovation programme under grant agreement n°SEP-210591007.
Sediment analysis. Point counting was conducted on photomicrographs of 152 thin sections of unconsolidated sediments from Hamelin Pool taken using a petrographic microscope in plane-polarized and cross-polarized light. Photomicrographs were visualized in JMicroVision Image Analysis Toolbox 1. 2. 7, and 300 randomly distributed points were counted on each slide. Results from this data set were originally reported in Suosaari et al. 2  using seven categories including: quartz, peloids and/or intraclasts, ooids and coated grains, bivalves, gastropods, foraminifera, and other. This study focuses on only the peloid data collected from that previous report and investigates the peloids and intraclasts independently.
Acknowledgements We thank the  Geological Survey of Western Australia  and  Bush Heritage Australia  for logistical support; and the  University of Miami  field team for field and technical assistance; and federal  Department of Environment and Energy  for field access and sampling permits. C. Mercadier would like to pay tribute to the memory of A. F. Maurin, whose visionary perception of microbial carbonate precipitation was an inspiration for his students. This project was funded by  Chevron ,  BP ,  Repsol  and  Shell. Hamelin Stromatolite Contribution Series #8.
, 2020; Kukla et al. , 2019; Mulch, 2016). Therefore, a rigorous reconstruction of the elevation history of the Alps requires careful consideration of both regional and global climate drivers. Modeling strategies using high-resolution isotope-enabled general circulation model (GCMs) together with time-specific boundary conditions have become a powerful tool not only for reconstructing global and regional paleoclimates but also for enhancing elevation reconstructions from δ 18 O p proxy data  (Botsyun & Ehlers, 2021). In this study, we complement previous work by providing high-resolution (T159, ∼0. 75° per grid cell) isotope-enabled GCM experiments with Middle Miocene boundary conditions and investigate temperature, precipitation, and the δ 18 O p pattern over the European continent. We compare model predictions with independent sources of information-first, with the result from models within the Miocene Model Intercomparison Project (MioMIP1), and second, with climate characteristics derived from proxy data.
, 2014). The compilation of mean annual precipitation (MAP) is presented separately in two tables (Tables  S1  and S2 ) depending on the estimation approach. Table  S1  contains MAP values reconstructed from fossil plant data using the coexistence approach. This table additionally contains estimates of monthly mean precipitation of the driest month (highest monthly precipitation, LMP) and monthly mean precipitation of the wettest month (lowest monthly precipitation, HMP) when available from the original publications. Table  S2  contains the herpetological assemblages presented in  Böhme et al. (2007 Böhme et al. ( , 2011)) , with addition of new sites from  Böhme and Vasilyan (2014). For both the paleotemperature and the paleoprecipitation databases, all site coordinates were rotated to their position in the middle Miocene using the GPLATES online tool (http://portal.
Acknowledgments This study was supported by the  German Research Foundation (DFG)  Grants  EH329/19-1 , 23-1 (to T. A. E. ),  MU4188/1-1 , 3-1 (to S. G. M. ),  DFG ME 4955/1-1  (to K. M. ), and  DFG MU 2845/6-1  (to A. M. ) as part of  DFG Priority Program MB-4D  ( SPP-2017 ). K. M. further acknowledges support through a  Feodor-Lynen-Fellowship  by the Alexander-von-Humboldt foundation. A. K. is supported through  Humboldt Research Fellowship  for experienced researchers by the Alexander-von-Humboldt foundation. The authors thank  R. Paul Acosta ,  Lin Li , and an anonymous reviewer for their constructive comments and recommendations, which highly improved this study. This work used computing resources of the  Deutsches Klimarechenzentrum (DKRZ)  granted by  its Scientific Steering Committee (WLA)  under project  bb1200.
Data Availability Statement The ECHAM model is available under the conditions of the "MPI-M Software Licence Agreement", which must be signed by each user. The "MPI-M Software Licence Agreement" and the information on the distribution of MPI-M models are given on: https://mpimet. mpg. de/fileadmin/projekte/ICON-ESM/mpi-m_sla_201202. pdf. The code of the isotopic version ECHAM5-wiso  (Werner et al. , 2011)  is available upon request on the GitLab repository of the Alfred Wegener Institute (Germany): https://gitlab. awi. de/mwerner/mpi-esm-wiso (registration and a login is required). The ECHAM5-wiso modeling results are available in netcdf format at  Botsyun et al. (2022).
MATERIALS AND METHODS The lithic assemblages studied in this work were recovered from stratigraphic layers of three archaeological sites: Tuina-5, Tambillo-1, and Tulán-67  (Núñez et al. , 2002; Núñez et al. , 2005)  (Table  1 ). In the first stage, a petrographic classification of the complete archaeological lithic assemblages was carried out. The object of this was to define general groups based on general rock classes and sub-varieties according to variables such as color, texture, granulometry, presence of impurities, and cortex, considering the existing local classifications 1 ,2,3. The second stage consisted of locating and characterizing the raw material sources through directed surveys in the field. The archaeological and geological data of the Atacama basin and its surroundings were consulted to define areas of interest according to the lithologies observed and recognized in the archaeological lithic assemblages.
The following data sourced were consulted: Calama sheet (1:250,000)  (Marinovic and Lahsen, 1984) , Zapaleri sheet (1:250,000)  (Gardeweg and Ramírez, 1985) , Toconao sheet (1:250,000)  (Ramírez and Gardeweg, 1982) , Chiu chart (1:50,000)  (Blanco and Tomlinson, 2009) , Lascar Volcano chart (1:50,000)  (Gardeweg et al. , 2011) , San Pedro de Atacama chart (1:100,000)  (Henríquez et al. , 2014) , Lila-Peine chart (1:100,000)  (Niemeyer, 2013). Between 2019 and 2021, various field surveys were carried out and samples were taken from the sources identified. The archaeological artifacts and the geological samples recovered from the lithic procurement sources were compared in the third stage using a magnifying glass (30x) and a Dino-Lite digital microscope (30-250x). Lithological markersunderstood as characteristic elements shared by rocks from the same outcrop-were defined that allowed the source of origin to be directly assigned.
Remotely-sensed Ocean color data offer a unique opportunity for studying variations of bio-optical properties which is especially valuable in the Arctic Ocean (AO) where in situ data are sparse. In this study, we re-processed the raw data from the Sea-viewing Wide Field-of-View (SeaWiFS, 1998(SeaWiFS,  -2010) )  and the MODerate resolution Imaging Spectroradiometer (MODIS, 2003(MODIS,  -2016) )  ocean-color sensors to ensure compatibility with the first ocean color sensor, namely, the Coastal Zone Color Scanner (CZCS, 1979(CZCS,  -1986)). Based on a bio-regional approach, this study assesses the quality of this new homogeneous pan-Arctic Chl a dataset, which provides the longest (but non-continuous) ocean color time-series ever produced for the AO (37 years long between 1979 and 2016). We show that despite the temporal gaps between 1986 and 1998 due to the absence of ocean color satellite, the time series is suitable to establish a baseline of phytoplankton biomass for the early 1980s, before sea-ice loss accelerated in the AO.
(2005)  who merged CZCS with SeaWiFS at the global scale. We provide the longest time series of climate-compatible AO-adapted satellite-based Chl a, an index of phytoplankton biomass identified as an 'Essential Climate Variable' (ECV) for the surface Ocean by the Global Climate Observing System (GCOS). This unique dataset was obtained by combining the data from the first satellite ocean color sensor, the Coastal Zone Color Scanner  (CZCS, 1979 (CZCS, -1986)) , with recent data from the SeaWiFS and MODIS sensors  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016). We addressed the issues related to the linkage between CZCS, SeaWiFS and MODIS data, that largely result from technical/instrumental limitations of the CZCS sensor.
These limitations include: (1) only four visible spectral bands (443, 520, 550, and 670 nm), (2) a 11-year data gap between the termination of CZCS and the start of the SeaWiFS operations, and (3) a poor spatio-temporal coverage of CZCS due to intermittent operations (i. e. , 2 h per day to limit power consumption). In this study, we re-processed the datasets obtained by the recent SeaWiFS and MODIS sensors using methods similar to the ones used for CZCS data in order to achieve coherence, homogeneity and to create a 37-year time series  suitable for climate change studies. We specifically applied the CZCS atmospheric correction procedure and Chl a algorithm to the SeaWiFS and MODIS raw data. The results from this challenging re-processing effort are extensively qualitycontrolled, and interpreted using an innovative bio-regional approach (Fig.
Data The CZCS Level-3 binned data at ~4. 6-km spatial resolution were downloaded from the NASA distributed Active Archive Center (DAAC) for satellite Ocean Biology (OB) by the Ocean Biology Processing Group (OBPG, https://oceandata. sci. gsfc. nasa. gov/), while the SeaWiFS and the MODIS archives were downloaded in Level-1A format and processed to Chl a (see section 3. 2). All datasets were re-projected on a sinusoidal ~27. 8-km resolution grid. In addition to ocean color data, daily composites of sea-ice concentration data were obtained from the National Snow and Ice Data Center for a grid resolution of 25 km 2 (dataset ID NSIDC-0051,  Cavalieri et al. , 1996). The dataset provides a consistent time series of sea-ice concentration between 1979 and 2016 obtained from brightness temperatures measured by several passive microwave instruments (SMMR, SSM/I, SSMIS).
'New' re-processed dataset (NEW) The processing used for the CZCS dataset has remained unchanged since the R2014. 0 re-processing (R2014. 0) carried out by the OBPG. This re-processing is similar to the processing applied to modern sensors like SeaWiFS and MODIS, except for specificities in the atmospheric correction procedure and assumptions used to determine the aerosol contribution to the atmospheric signal. CZCS was launched in 1978 as a "proof-of-concept" instrument  (Mitchell, 1994)  and was not equipped, unlike MODIS and SeaWiFS, with a pair of near infrared (NIR) bands (where water-leaving radiance contribution to the total signal reaching the satellite is very small to null) to determine the aerosol type and radiance contribution to the total top-of-atmosphere signal. Instead, CZCS relies only on a single wavelength at 670 nm (750 nm was not designed for ocean observations) to address atmospheric correction.
"CZCS-like" atmospheric correction uses an iteration scheme to account for possible contribution from the ocean to the total signal at the top of the atmosphere at 670 nm. The iteration scheme originally proposed by  Bricaud and Morel (1987) , was further developed by the OBPG (modified from  Stumpf et al. , 2003)  to include an assumption about the aerosol model that is used to extrapolate the aerosol contribution to the atmospheric signal at shorter visible wavelengths. Note that the same calibration strategy is now applied to all sensors since the R2006. 0 reprocessing  (Franz et al. , 2007; Werdell et al. , 2007). The 'CZCS-like' atmospheric correction was applied to SeaWiFS and MODIS Level-1B datasets (see step 1 to 2 in the flowchart in Fig. 2 ). This correction consisted of processing the entire SeaWiFS and MODIS datasets from all Level-1A source files, to Level-2 files using the same atmospheric correction that was applied to CZCS.
This approach further assumes a fixed aerosol model (r70f10, 10% fine mode fraction at 70% relative humidity as in  Ahmad et al. , 2010). Only pixels located above 45 • N were selected. Finally, the resulting Level-2 files were then temporally and spatially binned at the pixel level using averages to obtain merged daily L3 files at a spatial resolution of 4. 6 × 4. 6-km (step 2 to 3 in Fig. 2 ). This re-processing of SeaWiFS and MODIS data provided remote sensing reflectance (Rrs), from which Rrs(443) and Rrs(555) were used to estimate Chl a concentration. We used the band-ratio empirical algorithm for oceanic Case 1 waters from  Morel and Maritorena (2001)   ; step 3 to 4 in Fig. 2  and S1 ) following the recommendations of  Antoine et al. (2005)  who merged CZCS and SeaWiFS datasets for studies at temperate latitudes.
This Chl a dataset made of data from the three sensors (i. e. , CZCS, SeaWiFS and MODIS), with "CZCS-like" atmospheric correction and the empirical Morel-3 Chl a algorithm is referred to as the NEW dataset in the manuscript. The re-processing of the SeaWiFS and MODIS raw data required the equivalent of ~10 years of cumulated computing time on one processor (Intel Platinum 8260 Cascade Lake @ 2. 4Ghz). NEW was compared with a reference dataset (REF) which represents what was considered one of the most performant dataset for the AO. REF Chl a dataset includes SeaWiFS and MODIS and is based on the Garver-Siegel-Maritorena semi-empirical algorithm (GSM01,  Maritorena et al. , 2002; Maritorena and Siegel, 2005). This algorithm was selected because (1) it was found to perform better than standard empirical algorithms, such as the standard band ratio OC3/4 ocean color algorithm, especially in Arctic waters that are subject to high concentrations of Colored Detrital Matter (CDM) content due to freshwater inputs and resuspension of bottom sediment; (2) semi-analytical models such as GSM01 account for variations in CDM and particle backscatter yielding more accurate surface Chl a concentration compared to empirical models  (Ben Mustapha et al.
Outliers, data coverage and composite maps calculation Because the Morel-3 algorithm was developed for non-coastal waters and does not differentiate phytoplankton absorption from detritus or CDOM absorption (unlike the GSM01 algorithm), we masked nearshore areas with depth shallower than 25 m (see red area in Fig. 1A ) and Chl a Fig. 2. Schematic view of the data re-processing steps. All steps from 1 to 8 are described in the method section. This re-processing produces the 'NEW' dataset, in opposition with the 'REF' dataset which is our reference 'standard' dataset. higher than 15 mg m -3 (steps 4 to 5 in Fig. 2 ) in order to minimize pixels located in regions influenced by terrestrial inputs. Indeed, shallow areas are often associated with turbid waters contaminated by yellow substances or sediments especially during summer  (Matsuoka et al.
The combination of spatial binning (Fig. 3 AB ) and time-averaging (e. g. monthly, Fig. 3C ) resulted in a significant increase (by a factor of 10) in the data coverage of the CZCS dataset, making it comparable to the spatial coverage of the SeaWiFS and MODIS datasets. The existence of temporal gaps in the CZCS time series results from issues in the power demand from the nine sensors onboard the Nimbus-7 platform, which prevented their continuous operations. The CZCS sensor was operated on an intermittent schedule and collected data only two hours per day on average. One should be aware that missing data is an inherent issue when working with satellite ocean color passive sensors  (Racault et al. , 2014). This problem is especially acute in the Arctic due to a combination of polar night, sea-ice, and cloud cover, and is amplified by the poor spatial coverage of CZCS (Fig.
Chl a log-normal distribution As shown by  Campbell (1995) , Chl a follows a log-normal (base-10) distribution in the open ocean. We performed both Kolmogorov-Smirnov and Shapiro-Wilks tests to assess the normality of our Chl a dataset. The results showed a significant deviation from normality for both the linear and the log 10 -transformed datasets (p-value <0. 001). This result might be explained by the high sensitivity of such tests that account for very small deviations from the normality when dealing with very large sample size (N ~ 8 × 10 5 ). The Chl a log-normal distribution was therefore visually compared to the CZCS Chl a distribution (Fig. 4B ). We also computed statistics of skewness and kurtosis for annual climatological Chl a distribution. For the log-transformed distribution of Chl a, skewness and kurtosis ranged typically between -0.
46 and 1. 89 (CZCS skewness = -0. 07, CZCS kurtosis = 1. 41, Sea-WiFS skewness = -0. 46, SeaWiFS kurtosis = 1. 89, MODIS skewness = 0. 36, MODIS kurtosis = 1. 72), which is considered acceptable to prove normal univariate distribution (values are expected to vary between -2 and 2 according to Gravetter and Wallnau 2014). These indicators are respectively up to one order of magnitude higher for the distribution of Chl a in the linear space (CZCS skewness = 1. 89, CZCS kurtosis = 5. 19, SeaWiFS skewness = 2. 19, SeaWiFS kurtosis = 6. 52, MODIS skewness = 2. 20, MODIS kurtosis = 6. 61) than for the log 10 -transformed. Given these results, the Chl a dataset was considered to follow a lognormal distribution for this study.
Statistics for comparison of NEW vs. REF datasets To evaluate quantitatively the performance of the NEW Chl a reprocessing compared with the REF dataset (considered in this study as the reference measurements), a suite of statistical indices were derived as recommended by the Ocean Color Climate Change Initiative (OC-CCI; see  Brewin et al. , 2015; Evers-King et al. , 2017; Sathyendranath et al. , 2019). We computed the following set of statistical indices to assess the quality of the re-processed SeaWiFS and MODIS Chl a fields, which were previously log 10 -transformed. We derived the root-mean-square difference (RMS or Δ, Equ. 1) and bias (δ, Equ. 2) to report on dispersion and offset: Δ = ( 1 N ∑ N i=1 ( log 10 [ Chl 2,i ] -log 10 [ Chl 1,i ] ) 2 ) 1/2 (1) δ = 1 N ∑ N i=1 ( log 10 [ Chl 2,i ] -log 10 [ Chl 1,i ] ) (2) These indicators were associated with the unbiased root-meansquare (URMS) difference (Δ u , often referred to as centered Δ, Equ.
3) expressed on a log-10 scale: Δ u = ( 1 N ∑ N i=1 ( ( log 10 [ Chl 2,i ] -LC 2 ) - ( log 10 [ Chl 1,i ] -LC 1 )) 2 ) 1/2 (3) Where LC k indicates the average of the distributions (log(Chl k,i )) i =1, N for the dataset k. A small Δ indicates a small difference in magnitude between two datasets. The Δ coefficient consists of two components: i) δ representing the difference between the means (i. e. offset of the mean) and ii) Δ u representing the difference in variability (i. e. dispersion around the mean) between two datasets. Thus, bias δ and Δ u provide measures of how well the mean (i. e. accuracy) and the variability (i. e. precision) are represented and vary in time and/or space. We also calculated the Spearman's correlation coefficient r s , the slope S and the intercept I of the linear regression between the log 10 -transformed Chl a dataset using a standardized major axis (SMA) type II linear regression.