dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022) , which provides high-resolution and high-frequency temperature and salinity profiles all over the world's oceans, while the observation-based gridded products become more confident. The maximum spread between the period 1993-2019 occurs in the 1990s with a value of 0. 096 psu, and it decreases to as low as 0. 009 psu by the end of the 2010s. The mean salinity computed in the entire Mediterranean Sea from all products varies between approximately 38. 5 and 38. 6 psu with a spatiotemporal mean of 38. 57 psu (Table  2 ). In the western Mediterranean (Fig. 2b ), the overall mean is centred around 38. 16 psu with a larger spread -with a maximum and minimum of 0. 172 psu and 0. 026, respectivelyoccurring in the early 2000s. An increase of the mean salinity in 2005 is evident from all the reanalysis products and, to a lesser extent, from the CORA dataset, for which one of the many possible reasons is the regime shift as discussed in  Schroeder et al.
The trend in the mean of all products is calculated as 0. 0056 psu yr -1. This trend is consistent with the estimates between 1950-2002 of  Skliris et al. (2018)  Figure  1. The monthly reference salinity S ref estimates calculated from the MEDREA24 in the period 1993-2014. The green, blue and red curves show the MED, WMED and EMED regions, respectively on its corresponding vertical axis. The same calculation is done for each product separately (not shown) to evaluate Eq. (  2 ) to compute salinity anomaly. from the EN4 and MEDATLAS datasets, which show a trend of 0. 0096 ± 0. 0077 and 0. 0088 ± 0. 0092, respectively, in the first 150 m while showing a trend of 0. 0067 ± 0. 0040 and 0. 0067 ± 0. 0036 between 150-600 m. The differences in trend in different products that we used are mainly due to the discrepancies at the beginning of the time series.
Conclusions In this study, we presented the salinity characteristics of the Mediterranean Sea in the upper 300 m deduced from various products including reanalysis and gridded observational datasets released by the Copernicus Marine Service. The products with dynamically constructed ocean reanalysis and objectively analysed observations show significantly large spread at the beginning of the period of investigation, while the uncertainty is possibly reduced with the emergence of Argo profilers, which allowed wider spatial sampling and a higher frequency of sampling in the ocean. The mean salinity with its anomaly and trend is computed and analysed in the entire basin as well as in the western and eastern basins for all the datasets separately and averaged. The spatial maps of the mean and the spread of the salinity are depicted and discussed.
(2016) ; Tanay and Sebé-Pedrós  (2021) ). The genetic individuation of cell types then translates into phenotypic differences between cells, and one of the most intriguing questions is to determine this causation for each cell type. Only then will we understand their structural and functional peculiarities and intricacies and learn how evolutionary cell type diversification has sustained organismal morphology and physiology. As a prerequisite towards this aim, we need a comprehensive monitoring and comparison of cellular phenotypes across the entire multicellular body, which can then be linked to the genetically defined cell types. Recent efforts have focused on large-scale high-resolution imaging to capture the morphological uniqueness of cell types. For the first time, the rapid progress in volume EM allows this for increasingly large samples, including organs and entire smaller animals, with nanometer resolution  (Bae et   2021 )), such datasets allow characterization and comparison of cellular morphologies, including membrane-bound and membraneless organelles and inclusions, at an unprecedented level of detail for thousands of cells simultaneously  (Turner et al.
(2022) ; Vergara et al. (2021) ). A notable challenge of such large-scale studies is the analysis of massive amounts of imaging data that can no longer be inspected manually. The latest tools for visual exploration of multi-terabyte 3D data allow for seamless browsing through the regions of interest  (Pietzsch et al. (2015) ; Maitin-Shepard et al. (2021) ), given such regions are specified and a researcher has a clear study question in mind. However, if the goal is to characterise and/or compare morphologies of most or all cells in the dataset, one has to be able to automatically assign each cell an unbiased comprehensive morphological description -a feature vector with values representing the morphological properties of the cell in the most parsimonious manner. The need for automation and quantitative evaluation has triggered the design of complex engineered features that capture specific cell characteristics  (Barad et al.
(2022) ). However, such features often have to be manually chosen and mostly provide only a limited, targeted view of the overall morphology. To alleviate this problem, intricate modelling pipelines for cellular morphology quantification have been developed  (Ruan et al. (2020) ; Driscoll et al. (2019) ; Phillip et al. (2021) ), still these are often data-and task-specific. For unbiased monitoring and comparison of cellular morphologies, broadly applicable automated pipelines are missing. They should in addition be capable of extracting comprehensive representations that are not biased by manual feature selection, the end task or the type of data used. We expect that developing such techniques will not only speed up the process of scientific discovery in big volume imaging datasets, but also facilitate detecting unusual and underrepresented morphologies.
(2020) ; Van den Oord et al. (2018) ; Tian et al. (2020) ). Such methods have already shown to be successful for phenotyping cells in image-based profiling  (Lu et al. (2019) ; Lafarge et al. (2019) ) and for describing local morphology of patches or subcompartments within neural cells in 3D electron microscopy of brain volumes  (Huang et al. (2020) ; Schubert et al. (2019) ). Building on the latest achievements, we aim to expand the feature extraction methods to automated characterisation of cellular morphology at the whole animal level. Here we present the first framework for the fully unsupervised characterization of cellular shapes and ultrastructures in a whole-body dataset for an entire animal. We apply this new tool to the fully segmented serial blockface electron microscopy (SBEM) volume of the 6 days post fertilisation young worm of the nereid Platynereis dumerilii, comprising 11,402 mostly differentiated cells with distinct morphological properties  (Vergara et al.
The features are combined to form one feature vector for each cell of the animal. A common way to train a neural network for extracting relevant features is to use some prior information about samples in the dataset as a source of supervision. For example, to extract morphological representations of cells one could use a complementary task of predicting cell lines   (Kobayashi et al. (2021) ). However, such metadata is mostly unavailable for EM volumes, and manual annotation might become infeasible with increasing data size. Moreover, exploratory studies are often aimed at discovering unusual morphology, new cell types or tissues. In this case, defining supervision is not only difficult, but might also bias the exploration towards the "proxy" groups used for supervision. To enable immediate data exploration even for datasets where direct supervision is hard to define and the ground truth cannot easily be obtained, we developed a fully unsupervised training pipeline that is based on two complementary objectives (Figure  1B, C ).
The first is an autoencoder reconstruction loss, where a network extracts a low-dimensional representation of each cell and then uses this representation to reconstruct back the original cell volume. This loss encourages the network to extract the most comprehensive description. The second objective is a contrastive loss that ensures the feature vectors extracted from two similarly looking cells (positive samples) are closer to each other than to feature vectors extracted from more dissimilar cells (negative samples). Since we do not know in advance which samples can be considered positive for our dataset, we are using slightly different views of the same cell, generated by applying realistic transformations to cell volumes (see Methods). The combination of these two losses encourages the learned features to retain the maximal amount of information about the cells, while enforcing distances between feature vectors to reflect morphological similarity of cells.
MorphoFeatures allow for accurate morphological class prediction Good morphological features should distinguish visibly separate cell groups present in the data. To estimate the representation quality of our MorphoFeatures we quantified how well they can be used to tell such groups apart. We took the morphological cell class labels available in the dataset, proofread, corrected and expanded them. These labels include 7 general classes of cells: neurons, epithelial cells, midgut cells, muscle cells, secretory cells, dark neurosecretory cells and ciliary band cells (Figure  2A ) with ~60 annotated cells per class in the whole volume. It is worth noting that these cell classes were chosen based on visual morphological traits and do not necessarily represent genetically defined cell type families  (Arendt et al.
We also expect the approach to contribute to the analysis of cells in other volumetric modalities, such as X-ray holographic nano-tomography  (Kuan et al. (2020) ). For light microscopy with membrane staining, we expect the shape part of the MorphoFeatures pipeline to be directly applicable and a useful addition to existing analysis tools. Generally, the versatility of the pipeline is enhanced by its modularity that makes it possible to adapt different parts (e. g. , cell shape or nucleus fine texture) to datasets where not all the morphological components are desired or can be extracted. 2021 )) hints at supreme resolution power that appears to distinguish morphological groupings down to the cell type level (Figure  S3 ). For example, the subclusters of rhabdomeric photoreceptors of the adult eye and enteric neurons can not be identified in the explicit features representation.
Identification of cell types and cell type families with MorphoFeatures Most important, the cell clusters identified in MorphoFeature space appear to come closest to genetically defined cell types, as demonstrated here, among others, for different types of mechanosensory cells, or cell types of the apical nervous system. Interestingly, the MorphoFeatures also unravel morphological cell types without a genetic match in the cell type atlas (such as the enteric neurons, see above). This is most likely due to the yet limited coverage of the atlas and expected to improve with the continuous addition of genes to the atlas. In particular, the integration of other multimodal omics datasets such as single cell transcriptomics data will be highly rewarding to improve the match between morphological and molecularly defined cell types, which is the ultimate aim of cell type characterization and categorization  (Zeng and Sanes (2017) ).
Towards an unbiased whole-body identification of tissues and organs The current revolution in the generation of giant volume EM datasets including representations of entire bodies calls for new strategies in the automated processing and comparison of such datasets. This not only involves segmentation and characterization at the cellular level, but also requires the automated identification of discernable structures at the level of tissues and organs. So far, the recognition of tissues in larger volumes was achieved by manual segmentation. We now present a powerful new algorithm to recognize tissues and organs via their co-clustering in the Mor-phoContextFeature space, which takes into account similarities between neighbouring cells. Using this algorithm, we reproduce and partially outperform tissue recognition via manual segmentation -as exemplified for the ganglionic nuclei of the nereid brain.
Conclusion The revolution in multi-omics techniques and volume electron microscopy has unleashed enormous potential in generating multimodal atlases for tissues as well as entire organs and animals. The main unit of reference in these atlases is the cell type, which has been defined, and is manifest, both molecularly and morphologically. Our work now provides a versatile new tool to recognize cell types in an automated, unbiased and comprehensive manner solely based on ultrastructure and shape. For the first time, the MorphoFeatures will allow us to compare and identify corresponding cell types in datasets of the same species, but also between species, on purely morphological grounds. Furthermore, the MorphoContextFeatures automatically recognize tissues and organs across entire bodies, opening up new perspectives in comparative anatomy.
Data We used the dataset of the marine annelid Platynereis dumerilii at late nectochaete stage (6 dpf) from Vergara et al. (2021). The dataset comprised an SBEM volume of the complete worm in 10 × 10 × 25 nm resolution, 3D cell and nucleus segmentation masks as well as WMISH gene expression maps. The segmentation contains 11,402 cells with nuclei that belong to multiple genetically and morphologically distinct cell types. It is important to note that for neurons only somata were segmented, since the imaging resolution did not allow for neurite tracing. The gene expression maps cover mainly differentiation genes and transcription factors, which were registered onto the EM volume, exploiting high stereotypy of the platynereis at this stage of development. Gene expression values were also assigned to the EM segmented cells.
Preprocessing The EM dataset is used to extract the three morphological components separately for cytoplasm For this the bounding box surrounding cytoplasm or nucleus is split into cubes of 32 × 32 × 32 pixels, and only those cubes are considered, which are less than 50% empty. For both coarse and fine texture the data is normalised to the range of [0, 1] and for cytoplasm crops the nuclei are masked out and vice versa. In order to obtain point clouds and normals of the membrane surfaces, we first converted the segmented 3D voxel volumes into triangular meshes. More specifically, we first applied the marching cubes algorithm  (Lorensen and Cline, 1987; Van der Walt et al. , 2014)  followed by Laplacian smoothing  (Schroeder et al. , 1998)  to obtain smooth triangular surface meshes. These meshes were then simplified and made watertight using Stutz and Geiger (2020); Cignoni et al.
Classification of visibly distinct groups of cells In order to verify that MorphoFeatures can distinguish visibly different groups of cells, we evaluated the prediction accuracy of a logistic regression model that was trained on a small dataset of 390 annotated cells taken from Vergara et al. (2021) (Figure  2A ). The original annotation contained 571 neurons, 141 epithelial cells, 55 midgut cells, 53 muscle cells, 51 secretory cells, 41 dark cells and 25 ciliated cells. We first corrected some cells with clearly wrong assignments. Then for the classes that contained less than 60 cells we manually annotated more cells. From bigger classes we selected 60 cells, selecting the most morphologically diverse ones when possible. For ciliated cells we were only able to locate 30 cells in the whole animal.
First we computed the MorphoFeatures representation for each cell in the animal (excluding cells with severe segmentation errors) and standardised features across the whole dataset. We then trained and validated a logistic regression model using a stratified k-folds cross validator with 5 folds. We used the implementations of logistic regression, stratified k-fold from Scikit-learn (Pedregosa et al. , 2011) with the following arguments: C=1, solver='lbfgs'. To prevent potential overfitting due to the high number of features we used feature agglomeration  (Pedregosa et al. , 2011)  (n_clusters=100) to reduce the amount of similar features.
Acknowledgments We thank all the members of the  Kreshuk and Uhlmann group , especially  Constantin Pape , for helpful comments. We further thank  Christian Tischer  for implementing features essential for biological analysis and exploration in MoBIE. We thank  Hernando M. Vergara  for assistance in cell type recognition and  Kimberly I. Meechan  for help with comparison to explicit features. We would also like to acknowledge the support of the  EMBL IT Services , especially  Juri Pecar  for maintaining the GPU resources. The work was supported by  EMBL  internal funds, and by a grant from the  European Research Council  ( NeuralCellTypeEvo 788921 ) to D. A.
ariant diameter, major & minor axes, surface area, sphericity, max radius), intensity features (different quantiles of intensity mean, median and standard deviation) and Haralick texture features. These features were calculated, whenever appropriate, for cell cytoplasm, nucleus and chromatin segmentation. The calculated features were taken from https://github. com/mobie/platybrowser-pr oject/blob/main/data/1. 0. 1/tables/sbem-6dpf-1-whole-segmented-cells/morphology. tsv for cellular segmentation and from https://github. com/mobie/platybrowser-project/blob/main/data/1. 0. 1/tables/ sbem-6dpf-1-whole-segmented-nuclei/morphology. tsv for nucleus and chromatin segmentation. The UMAP of these features was taken from https://github. com/mobie/platybrowser-project/blob/main/ data/1. 0. 1/tables/sbem-6dpf-1-whole-segmented-cells/morphology_umap.
com/mobie/platybrowser-project/blob/main/data/1. 0. 1/tables/sbem-6dpf-1-whole-segmented-cells/ga nglia_ids. tsv and https://github. com/mobie/platybrowser-project/blob/main/data/1. 0. 1/tables/sbem-6 dpf-1-whole-segmented-cells/regions. tsv and gene clusters from https://github. com/mobie/platybrowse r-project/blob/main/data/1. 0. 1/tables/sbem-6dpf-1-whole-segmented-cells/gene_clusters. tsv (Vergara et al. (2021) ).
Tree altitude values were computed from the airborne laser scanning data (see Section 2. 2. 2) by bilinear interpolation of the ground-classified points at the location of trees. Figure  2  shows a map of inventoried live trees and canopy heights. In the forest stand inside the fence, most of the trees are between 30 and 40 m high (Figure  3 ) and the total basal area is about 66. 3 m2/ha. It includes 52 firs, 43 spruce and 33 broadleaved trees. Trees measured outside the southern fence are 9 spruce, 2 firs and 7 broadleaves. Tree position accuracy is estimated to be better than 10 cm for the trees measured with the total station inside the fenced area (it is estimated to be around 50 cm for additional trees outside the fence). Luoma et al. (2017)  reported a precision of 0. 5 m (standard deviation) for height measurements with a Vertex 4 clinometer.
Airborne remote sensing measurements Airborne laser scanning (ALS) is a remote sensing technique based on LIDAR which can provide a 3D point cloud of forest structure. The geometric information on the vegetation can be processed to derive forest metrics used to parametrize snow interception (e. g. ,  Helbig et al. , 2020). ALS was acquired during a campaign covering 123. 5 km 2 between 30 August and 2 September 2016, using a Riegl LMS Q680i sensor mounted on a helicopter. The scheduled flight height and speed were 750 m above ground and 70 knots, respectively. The scan frequency was 300 kHz with a scan angle of ± 30°. The aircraft trajectory was computed from the Inertial Measurement Unit and GPS data. Point coordinates were extracted and computed using the RiAnalyse and RiWorld software. The point cloud was then classified as ground / non-ground using Terrasolid.
Weather station The weather station was installed under rather dense canopy during the first campaign and has been maintained since then. Table  1  lists the sensors installed on the station, their specifications and their accuracy according to the manufacturer. The during daytime. These images are used to monitor surface and sensor conditions. A Campbell Scientific IR120 infrared sensor was used to measure the surface temperature of a trunk close to the meteorological station (Figure  1 ). One-minute averages of longwave irradiance measured at five second time intervals were recorded by a Campbell Scientific CR1000 data logger. Table  1 : variables measured by the weather station below the canopy along with the sensor type, heights and precision according to the manufacturer. in the forest (Table  1 ) from 1 September 2018 to 1 June 2019.
Spatial variability, measurement uncertainties and data validation This section provides estimations of the dataset uncertainties related to measurement uncertainties and spatial variability of the variables within the measurement plot. The uncertainties on the sensors and on the measurement methods have been described in the previous sections. For meteorological measurements, sensor manufacturers generally provide reliable information on sensor accuracy (Table  1 ). In this Section, comparisons of radiation, air temperature, and snow measurements at different locations provide a better insight into the measurement uncertainties and a first validation of the data set. suggest that the effects of the forest on snow cover are more marked during the winter accumulation season (likely due to interception of snow by the canopy), whereas melt rates during the short ablation season appear to be quite similar in the forest and the meadow.
Conclusions The datasets collected in the Col de Porte coniferous forest will allow research on the effects of the canopy on snow accumulation and ablation processes under different canopy covers. Two intensive field campaigns were conducted during the winters of 2016-17 and 2017-18 and an automatic weather station has been maintained under the canopy since then. Meteorological and snow measurements (automatic weather station, radiometer array, snow pole transect, laser scan, precipitation tanks to estimate snow interception by the canopy) were complemented by canopy observations (tree inventory, LIDAR measurements of forest structure, sub-canopy hemispherical photographs). Continuous measurements throughout the year at high temporal frequency (15-minute) from the meteorological station allow hydrological and ecological studies related to seasonal changes in micrometeorological and soil conditions.
Data availability All datasets described and presented in this paper can be openly accessed from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at: http://dx. doi. org/10. 17178/SNOUF. 2022  (Sicart et al. , 2022). Table  2  provides the links to the different datasets. https://doi. org/10. 5194/essd-2023-174 Preprint. Discussion started: 1 June 2023 c Author(s) 2023. CC BY 4. 0 License.
After drying, the filters were placed in a desiccator to cool to room temperature and weighed. This process was repeated three times to ensure a complete loss of moisture content. The difference between pre-and post-filtering of dry weights divided by the filtered water volume provided the TSS (Van der Linde, 1998). These in-situ data were used to determine empirical relationships between CDOM and TSS and water surface remote sensing reflectance (Section 2. 3) to enable satellite-based retrieval for NHR runoff characterization.