dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq RNA-seq experiments were performed as previously described  72  with minor modifications. RNA from 1 million cells was isolated using Trizol LS reagent (Thermo Fisher Scientific, 10296010) followed by rRNA depletion and strand-specific library construction according to the manufacturer's instructions (Illumina). Multiplexed Illumina sequencing was done using 86 bp single-end reads on the Next-Seq 550 system (Illumina) according to the manufacturer's instructions at the high-throughput sequencing core facility of the I2BC (Gif-sur-Yvette, France). Data was mapped to the ENSEMBL Mouse assembly GRCm38. p6 (mm10) using STAR (v2. 4. 2a) with default parameters  97.
Acknowledgements We thank  Alessandra Montecucco  ( IGM-CNR, Pavia, Italy ) and members of the  Noordermeer lab  for useful discussion. We thank  Joke van Bemmel ,  Edith Heard ,  Elphège P. Nora ,  Benoit Bruneau ,  Maxim Greenberg ,  Ning Qing Liu , and  Elzo de Wit  for sharing of mESC lines. We thank  Benoit Moindrot  ( I2BC, Gif-sur-Yvette, France ) for help with Western blotting. This work has benefited from the facilities and expertise of the highthroughput sequencing core facility of the I2BC (Gif-sur-Yvette, France). D. N. and E. F. J. benefit from collaborative funding from the  Agence Nationale pour la Recherche  ( ANR-21-CE12-0034-01 ) /  National Science Foundation (NSF). D. N. and D. H. benefit from collaborative funding from  PlanCancer  ( 19CS145-00 ). D. N. was further supported by the  Agence Nationale pour la Recherche  ( ANR-18-CE12-0022-02 ,  ANR-17-CE12-0001-02 ,  ANR-16-TERC-0027-01 ,  ANR-14-ACHN-0009-01 ), the  Fondation Bettencourt Schueller , the  Fondation de Coopération Scientifique Campus Paris-Saclay  ( 2015-0980i ) and  Oxford Nanopore Technologies  ( MinION Early Access Program and Direct-RNA Early Access Program ).
S. G. and A. P. were supported by postdoctoral fellowships from the  Fondation pour la Recherche Médicale  (Post-doctorat en  France -SPF201909009328  and  SPF201909009284 ). Cryo-EM experiments were supported by the  CNRS network Microscopie Electronique et Sonde Atomique  ( METSA ,  FR CNRS 3507 ) to D. N. and the  Investissements d'Avenir LabEx PALM  ( ANR-10-LABX-0039-PALM ) to A. L. J. M. L. was supported by the  National Institute of Health  ( F31HD102084 ). E. F. J. was supported by the  National Institute of Health  ( R35GM128903  and  U01DA052715 ). D. H. was supported by the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program  ( 882673 ).
Data availability The data that support this study are available from the corresponding author upon request. The unprocessed Oxford Nanopore Technologies and Illumina sequencing data generated in this study (Nano-C, ChIP-seq, 4C-seq, RNA-seq) have been deposited in the European Nucleotide Archive (EMBL-EBI ENA) database under accession code PRJEB44135. The processed sequencing data (Nano-C, ChIP-seq, 4Cseq, RNA-seq) and Cryo-EM images are available at the Mendeley Data repository under accession code 10. 17632/g7b4z8957z. 4. Unprocessed and processed data are available without restrictions.
, 2020). Satellite Earth observations provide a means of accurately assessing the carbonate content of large rivers by using satellite-observed oceanographic variables with published empirical algorithms that link carbonate system variables to the satellite-derived variables  (Land et al. , 2019). The Satellite Oceanographic Datasets for Acidification (Ocean-SODA) project (https://esa-oceansoda. org, last access: 10 August 2022) was established to further develop these approaches. We present the University of Exeter (UNEXE) OceanSODA dataset (OceanSODA-UNEXE), which comprises decadal datasets of riverine carbonate system variables for the two largest rivers in the world by discharge, the Amazon and the Congo  (Dai and Trenberth, 2002). This paper details how the optimal combinations of empirical algorithms and Earth observation datasets were selected and used to construct OceanSODA-UNEXE.
Statistical terms overview Root-mean-squared difference (RMSD) is a measure of accuracy and is calculated as the square root of the average of squared errors, e. g. RMSD = (( (x 0 -x 1 ) 2 )/n) 1/2 , where x 0 denotes the estimated values, x 1 denotes the reference values and n is the number of observations. The bias of a dataset is defined as the mean difference between the estimation and reference, Bias = (x 0 -x 1 ))/n. The mean absolute difference (MAD) of a dataset is a measure of variability and is calculated as MAD = ( |x -x|)/n, where x is the mean. Earth Syst. Sci. Data, 15, 2499 Data, 15, -2516 Data, 15, , 2023  https://doi. org/10. 5194/essd-15-2499-2023 The correlation coefficient (r ij ) is a measure of linear correlation between the estimate and the reference variables and is defined as   , 2008).
r ij = ( (x i -x i )(x j -x j ))/( (x i -x i ) 2 (x j - x j ) 2 ) Weighted statistics allow uncertainties in the reference dataset (in situ measurement uncertainty in this case) to be accounted for within the performance analysis (i. e. the reference data are not considered "truth", as they also contain uncertainties). Weights are calculated as the sum of the individual weight of each algorithm (w), where w = 1/((in situ measurement uncertainty) 2 + (literature algorithm uncertainty) 2 ) 1/2  (Ford et al. , 2021). For clarity and easy comparison to previous published work, both weighted and unweighted statistics of all metrics are given, e. g. weighted RMSD (wRMSD) is calculated as wRMSD = ( (weights * (x 0 -x 1 ) 2 )). When evaluating algorithms using a statistical measure, in this case wRMSD, a further issue can arise where the valid region over which each algorithm can be applied overlaps with different in situ data.
The regional bounds of the Amazon outflow were defined as being 2 • S-24 • N and 70-31 • W. The bounds of the Congo outflow were defined as being between 10 • S-4 • N and 2 • W-16 • E. To be included in the algorithm evaluation, algorithms needed to be applicable to these regions and to take the form of a linear or quadratic relationship with input variables that were easy to obtain and were available as spatially and temporally varying datasets. These input variables included sea surface temperature (SST), sea surface salinity (SSS), potential temperature (which is assumed to be approximately equal to SST at the surface), dissolved oxygen (DO), nitrate (NO - 3 ), phosphate (PO -3 4 ), silicate (SiO -4 4 ) and chlorophyll a. A total of 26 of the identified algorithms were not included in the algorithm evaluation, as they could not generate TA or DIC using the accessible input variables listed above or were not based on empirical algorithms (see Table  S2  in the Supplement for the full list).
Algorithm evaluation A multipurpose global reference matchups database (MDB), matching in situ carbonate system parameters from the surface 10 m with satellite, model and interpolated in situ datasets, was used to perform the algorithm evaluation  (Land et al. , 2023). The MDB is optimized to reduce biases arising from uneven data density; this is achieved by grouping in situ observations into 100 km diameter regions of interest (ROI) which span a 10 d time period. The MDB was constructed from global datasets that include all of the variables required for all algorithms (SST, SSS, θ , DO, NO   (Merchant et al. , 2019; Good et al. , 2019) , Optimum Interpolation SST (OISST) v2. 1  (Huang et al. , 2021; Banzon et al. , 2016)  and the Coriolis Ocean dataset for Reanalysis (CORA) v5. 2  (Szekely et al.
, 2019)  -and four global SSS datasets -the European Space Agency Climate Change Initiative SSS (ESACCI SSS) v2. 31  (Boutin et al. , 2020 (Boutin et al. , , 2021)) , CORA v5. 2  (Szekely et al. , 2019) , the In Situ Analysis System 15 (ISAS15)  (Kolodziejczyk et al. , 2021; Gaillard et al. , 2016)  and the Remote Sensing Systems data from the Soil Moisture Active Passive satellite (RSS-SMAP) level 3 v4. 0  (Meissner et al. , 2019 (Meissner et al. , , 2018)). The MDB also contains TA, DIC and pH values, most of which come from the Global Ocean Data Analysis Project (GLODAPv2. 2020) database  (Olsen et al. , 2016) , and pCO 2 values, most of which come from SOCAT v2020  (Bakker et al. , 2016) , along with additional data for Arctic waters. A full list of references for data variable sources used in the MDB can be found in  Land et al.
(2023). Before beginning this analysis, we limited our analysis to only use MDB data that fall within the following bounds: SST > -10 or < 40 • C, SSS > 0 or < 50, DIC > 500 or < 3000 µmol kg -1 , TA > 500 or < 3000 µmol kg -1 , pH > 6 or < 8. 5, and pCO 2 > 100 or < 3000 µatm. These constraints are consistent with the conditions likely within the Amazon and Congo regions. Each valid algorithm was implemented by closely following the specific literature recommendations; this was done in such a way that empirical algorithms were only assessed for the geographical and geophysical ranges for which they were originally developed. The algorithm evaluation involved running each algorithm with inputs from the MDB to estimate the TA and DIC outputs (termed the "algorithm output"). Each algorithm output was then evaluated using the TA and DIC values from the MDB matchup database (termed the "reference output").
The RMSD, bias, correlation coefficient and MAD are calculated from the algorithm output and the reference output. Additionally, wRMSD was calculated for algorithms for which an RMSD is reported in the literature. Where a wRMSD was calculated and the matchups (n) were > 30, an RMSDe was calculated for those algorithms following the methodology of  Land et al. (2019). As the MDB includes three global SST and four global SSS datasets, each algorithm was run for each of the 12 combinations of SST and SSS input datasets, and separate statistics were generated for each configuration. The subset of input variables from the MDB used to implement the algorithm, the algorithm output and the reference output for all 12 SST and SSS combinations are included in the Supplement. Additionally, summary statistics between the algorithm output and the reference output are also provided in the Supplement; these statistics are the mean, the weighted mean (wmean), the standard deviation, the RMSD, the weighted RMSD (wRMSD), the RMSDe, the Bias, the weighted Bias (wBias), r, the weighted r (wr), the MAD and the weighted MAD (wMAD).
The best performing TA and DIC algorithms for both regions were determined by ranking the algorithms by RMSDe: the best performing algorithms were the algorithms with the lowest RMSDe values. In order to select algorithms suitable for generating time series, observation datasets for SSS and SST needed a temporal overlap of at least 8 years. The remainder of this paper exclusively discusses the time series generated with these "optimal algorithms". The algorithm evaluation process follows a standardized framework for a Type-A uncertainty evaluation for TA and DIC, whereby the following sources of uncertainties are considered: 1. the TA or DIC measurement uncertainty -measurement uncertainty for both TA and DIC is typically stated as ± 4 µmol kg -1 , but (following  Bockmon and Dickson, 2015)  we used a more conservative measurement uncertainty of ± 10 µmol kg -1 (approximately equal to 0.
The uncertainty estimates provided within the dataset are the combined standard uncertainty (δQ), and these combined standard uncertainties are reported without confidence intervals. To aid in the interpretation of the combined uncertainty budgets, a second Type-A uncertainty evaluation of the TA and DIC approaches was calculated based purely on the empirical algorithm (e. g. literature RMSD value) and the uncertainties in the input data propagated using standard techniques  (Taylor, 1997)  and assuming that any uncertainties were uncorrelated. This is a second independent Type-A uncertainty evaluation that does not include uncertainties due to spatial variability and depth. Assuming that the first Type-A uncertainty evaluation has captured all of the uncertainties, the difference between the two uncertainty evaluations enables the estimation of the contribution of the spatial and Earth Syst.
Creation of a gridded monthly time series product The full-spatiotemporal-resolution SST and SSS datasets and gridded World Ocean Atlas (WOA) DO, NO - 3 , PO -3 4 and SiO -4 4 datasets were downloaded from their online repositories. The total size of these full-resolution datasets occupies ∼ 500 GB of hard drive space and take several days to fully download. The SST and SSS datasets were uncompressed and were regridded onto a monthly 1 • × 1 • standard World Geodetic System grid for use in creating the gridded output; the grid has 180 • of latitude and 360 • of longitude with 0 • centred on the Greenwich meridian. Reformatting the datasets considerably reduced the file size to a more manageable ∼ 50 GB. The scripts used to download these SST and SSS monthly datasets are fully automated, meaning that new data can easily be incorporated into future versions of OceanSODA-UNEXE.
For each target variable (TA or DIC), the selected optimal SST and SSS gridded datasets and gridded WOA datasets were used as inputs for the respective TA and DIC algorithms. The algorithms were applied to the datasets in the two outflow regions where the SST and SSS datasets overlapped in time for the published environmental limits of each algorithm. The monthly SST and SSS inputs as well as the calculated monthly output variable (TA or DIC) are saved to a netCDF4 file as separate variables on the grid described above with dimensions of 768 × 180 × 360. The time dimension is the number of months since January 1957. It is important to note that TA and DIC datasets (even if for the same region) can span different temporal domains and should, therefore, be treated as separate datasets. These differences are due to differences in the temporal range of the input data used to calculate them.
The oceanic carbon system equations  (Millero, 2000)  can be solved computationally  (Lewis et al. , 1998)  using numerical software packages  (Orr et al. , 2015) , and the established CO2SYS software package is now available in Python as PyCO2SYS  (Humphreys et al. , 2022). Where there was temporal overlap between TA and DIC in each region, the remaining oceanic carbonate system variables (pCO 2 and pH) were calculated using PyCO2SYS v1. 71  (Humphreys et al. , 2022). A number of other output variables were also calculated including fCO 2 , the carbonate ion content (CO -2 3 ), the bicarbonate ion content (HCO - 3 ), the hydrogen ion content (H + ), and the calcite ( calcite) and aragonite ( aragonite) saturation states. PyCO2SYS was run with the selected optimal dataset (SST, SSS and WOA datasets where applicable, with the same data used for input and output conditions) at the surface (0 m depth) with the carbonic acid dissociation constants of  Mehrbach et al.
(1973)  refitted by  Dickson and Millero (1987)  and the hydrogen sulfate dissociation constant of  Dickson (1990). The full carbonate system is calculated twice: once using the optimal SST and SSS datasets that were used to calculate TA and then again employing those used to calculate DIC. Consequently, each netCDF file contains a complete set of carbonate system outputs that are unique to that specific input dataset. Whilst some of the SST and SSS products used to create the gridded datasets contain only satellite-observed SST at a specified depth, some of the reanalysis SST and SSS products also incorporate surface ocean measurements from ships and buoys that were made at differing depths. The TA and DIC data used to generate the MDB come from the top 10 m of the ocean and have not been adjusted for potential concentration gradients in the surface ocean  (Land et al.
Amazon dataset The primary output datasets are the monthly gridded carbonate system variables (DIC and TA). The seasonal progression of both these variables is shown in the Amazon outflow region in Figs. 2  and   , 3b ), which is a feature that has been reported elsewhere  (Chérubin and Richardson, 2007). From July to September (Figs. 2c, 3c ), another large outflow region occurs just off the coast of Venezuela at the mouth of the Orinoco River  (Hu et al. , 2004). The Orinoco plume is much less prominent in the other seasons. The proximity of the Orinoco River to the Caribbean suggests that the Orinoco plume often reaches the islands between July and September  (López et al. , 2013). There are some differences between the structure of the average TA and DIC plumes that may indicate active processes in the plume, such as gas exchange, which only affects DIC, or biological production, which affects TA and DIC at different rates.
Annual and seasonal data averages over the region shown in Figs. 2 and 3 are provided in Table  S3  in the Supplement for DIC, TA, pH, pCO 2 , calcite and aragonite. The temporal aspect of the dataset is presented in Fig. 4  as a meridional Hovmöller plot at 52 • W (marked in Figs. 2  and 3 ) which broadly cuts through the central plume outflow at 4 • N, the mixing of the plume up to 10 • N and the open-ocean Atlantic water > 10 • N. The lowest DIC values (∼ 1400 µmol kg -1 ) are found in the plume at 4 • N, and there is regular seasonality with lower values in October-December and higher values in April-June (Fig. 4a ). There does not appear to be much inter-annual variability in the   S1. Each SST and SSS dataset input configuration for which algorithms were evaluated is shown for each algorithm, with each SSS dataset given by a different symbol and each SST dataset given as a different colour in the legend.
76 µmol kg -1 yr -1. It should be noted that the whole region is out of phase with the plume; this is because, whilst the plume is more dilute, this effect is outweighed by the larger plume between May and August. For pH, there is a weak seasonal trend outside the plume. In contrast there is a lot more variability in the plume, with the highest pH values (∼ 8. 225) occurring in December and the lowest values (∼ 8. 2) in September. The best SST and SSS datasets from the DIC algorithm predict that the pH will be > 0. 1 higher than the best SST and SSS datasets from the TA algorithm between January and March. The two estimates agree within < 0. 1 pH units for the other 9 months of the year. For pCO 2 , also computed from TA and DIC, mean values in the non-plume region and over the whole region were very stable, which is consistent with the minimal season variability seen in oligotrophic oceans.
Whereas there  was considerable variability in plume pCO 2 , with average values of ∼ 325 µatm seen in the plume in March and values of ∼ 350 µatm in August, the differences between the pCO 2 calculated with the different SST and SSS datasets was very small, with differences < 5 µatm most of the year. The average calcite and aragonite saturation states are in the typ-ical range for seawater and are much greater than the critical thermodynamic threshold for calcification with a value of one  (Waldbusser et al. , 2016). It should be noted that an aragonite saturation state threshold of three has also been recommended for warm-water corals  (Guinotte et al. , 2003) , and other studies have since shown that warm-water corals can still adapt and survive in these conditions  (Enochs et al. , 2020; Uthicke et al.
Congo dataset In the Congo outflow region, there is a strong seasonal variation in DIC (Fig. 6 ). From July to September (Fig. 6c ), there are two regions of low DIC: one directly at the outflow point of the Congo River and another at the outflow point of the Niger River delta. The outflows of both of these river-dominated regions extend directly west, aligned with the South Equatorial Current in the eastern Atlantic  (Hopkins et al. , 2013). In October-December (Fig. 6d ), the spatial influence of the Congo outflow is smaller, possibly due to water masses moving northwards along the coast. Into January-March (Fig. 6a ), there is a region of low DIC along the whole coast, representing the intensification of the Niger and Congo discharges at this time of year  (Chao et al. , 2015). In April-June (Fig.
This consistency is also seen in the propagated variables, pH (Fig. 9c ), pCO 2 (Fig. 9d ), and the aragonite and calcite saturation states (Fig. 9e, f ), all of which show very minor differences between the outflow and non-outflow regions. datasets (2002-2016). Data are averaged across the whole region (solid line) as well as in the outflow defined as S < 35 (dashed line) and outside of the outflow S > 35 (dotted line). The line colour corresponds to variables that were calculated with the SST and SSS datasets selected during the DIC algorithm evaluation (red) and the TA algorithm evaluation (blue).
Uncertainty assessment The TA and DIC combined standard uncertainties from the algorithm evaluation are shown in Tables  1  and 2. The combined standard uncertainties for the optimal algorithms used to generate the time series dataset are reproduced in Table  3. The combined standard uncertainties are the first Type-A uncertainty evaluation, which can be thought of as a top-down estimate. The spatially averaged uncertainties of the remaining carbonate system variables using this top-down estimate are shown in both outflow regions (Table  3 ). The uncertainties of the remaining carbonate system variables appear to be weakly dependent on the choice of the SST and SSS datasets used to calculate them in PyCO2SYS (Table  3 ). Uncertainties are provided as absolute values, rather than as percentages, because of the steep gradient in values across the plume; however, if required, the uncertainty values can be expressed as a percentage using the minimum, maximum and mean values from Table  S4  in the Supplement.
The second Type-A uncertainty evaluation (using the algorithm RMSD from the literature and the uncertainties in the https://doi. org/10. 5194/essd-  15-2499-2023  Earth Syst. Sci. Data, 15, 2499-2516, 2023 As the top-down uncertainty evaluation is more robust, it is the preferred uncertainty estimate. By comparing the uncertainties with the natural variability in TA and DIC (Table  4 ), it is clear that the dataset uncertainty is less than the natural variability in data in both riverine regions. Although the propagated uncertainties for pCO 2 and pH are larger than the natural variability, this is only because the pCO 2 and pH data generated in OceanSODA-UNEXE have full spatiotemporal coverage, including in the high-variability plume, compared with the pCO 2 and pH in situ data in the MDB.
Discussion The algorithm evaluation (Fig. 1 ) demonstrated that the choice of input SSS dataset to the TA and DIC algorithms makes the biggest difference in reducing the RMSDe in TA and DIC. SST tends to be a secondary term in the majority of the algorithms and, therefore, has less of a controlling effect. The choice of the literature TA or DIC algorithm itself impacts RMSDe to a much smaller extent than the choice of input SSS datasets. The prominence of SSS terms in the majority of the algorithms explains why the RMSDe is much more sensitive to the choice of SSS dataset compared with SST dataset, as SST is a secondary term in the majority of the algorithms. Whilst some algorithms did perform better than others, the differences were so slight that it may not be that helpful to declare which of the algorithms are the best outright, especially as this could change in the future with more data.