dataesr/scientific-paragraphs-categorization huggingface dataset:
Altimetry-based sea level data Two sources of data have been used: 1. The Climate Change Initiative (CCI) sea level product (http://www. esa-sealevel-cci. org) from the European Space Agency (ESA) from January 1993 to December 2015 (version 2. 0). This product combines data from the TOPEX/Poseidon, Jason-1/2, GFO, ERS-1/2, Envisat, CryoSat-2 and SARAL/Altika altimetry missions and is based on a new processing system with dedicated algorithms and adapted data processing strategies  (Legeais et al. , 2018). It is available as a gridded 1/4   (2017). This consists of removing a trend of -1. 0 mm/yr over January 1993 to July 1995 and + 3. 0 mm/yr over August 1995 to February 1999. The glacial isostatic adjustment (GIA) correction is applied (a value of -0. 3 mm/yr is considered, Peltier, 2004).
2. The sea level data from the Goddard Space Flight Center (GSFC) (https://sealevel. nasa. gov/understanding-sea-level/key-indicators/ global-mean-sea-level/). The GSFC data set is based on the TOPEX/ Poseidon, Jason-1, Jason-2 and Jason-3 data averaged over the 66 • S -66 • N domain directly from the along-track sea surface height data. To account for the TOPEX-A altimeter drift, the internal calibrationmode range correction, included in the TOPEX 'net instrument' correction, (supposed to be suspect owing to changes in the altimeter's point target response) has been suppressed from the GSFC data set  (Beckley et al. , 2017). It is also corrected for GIA, considering a value of -0. 3 mm/yr. The GMSL data sets have been analysed over January 1993 to December 2019, i. e. , a complete 27-yr long time span.
Climate modes In this study, we considered the following climate indices: MEI, PDO, IOD, NAO and AMO. MEI is the Multi variate ENSO index representing El Niño Southern Oscillation. It combines information of surface pressure, sea surface temperature, surface wind and outgoing long-wavelength radiation over the tropical Pacific. PDO and IOD are derived from sea surface temperature data, respectively north of 20 • N in the Pacific, and over the Indian Ocean. NAO is defined by the sea level pressure difference between Azores and Iceland, while AMO represents the average sea surface temperature of the North Atlantic Basin. The corresponding time series have been downloaded from publicly accessible data websites (date of download: 22 December 2020):  The time span January 1993 to December 2019 has been considered for the climate indices, as for the GMSL time series.
2 ). For that purpose, we have used the Lomb-Scargle periodogram. The Lomb-Scargle periodogram is a method that allows efficient computation of a Fourier-like power spectrum estimator for detecting and characterizing periodic signals in unevenly sampled data   (e. g. , VanderPlas, 2018). We use the plomb function provided by MATLAB to calculate the power spectral density estimate of the quadratically detrended time series of interest. As sample rate, we consider 12 samples per year. We represent the logarithm of the multiplicative inverse of the frequency, which is the period in years. We also estimate probability of detection of the estimated period, that measures the peak significance level. We set the threshold at the 95% confidence level. The detection probability shows the probability that a peak in the spectrum is not due to random fluctuations.
Fig. 2  displays dominant peaks around 3 years and 12-13 years. The GSFC periodogram also shows a peak at 6-7 year, that is only barely significant in the CCI/CMEMS spectrum. Since our initial objective was to investigate how the GMSL rate evolves with time, we next compute the GMSL rate over successive 3year, 4-year and 5-year windows shifted by 1-month, considering both the CCI/CMEMS and GSFC data sets. The corresponding curves are shown in Fig. 3. The rate uncertainty estimation is based on the Deming regression  (Deming, 1943; Wu and Yu, 2018)  in which a linear fit is computed accounting for the uncertainties of the variables, in our case the uncertainty in the GMSL proposed by  Ablain et al. (2019). The best slope and intercept are computed by minimizing the chi-square calculated using both standard deviation on the dependent and independent variables.
The uncertainty of the fitted parameters is computed using a Monte Carlo simulation with 1000 iterations, assuming that errors are Gaussian and centered. The algorithm used can be freely downloaded from the MATLAB Central File Exchange (https://www. mathworks. co m/matlabcentral/fileexchange/45711-linear-fit-with-both-uncertainti es-in-x-and-in-y) (retrieved 31 December 2020). Note that all uncertainties reported in the rates are given at the 1. 96-sigma level, i. e. , 95% confidence level. For each data set, we note that the three curves (corresponding to the 3-year, 4-year and 5-year windows) show an overall similar behaviour, i. e. , an irregular increase with time along with steep increases in  1995-2001 and 2009-2014. The 3-year window case displays much higher interannual oscillations, as expected.
We applied this approach to the CCI/CMEMS time series. Note that for this computation the climate index times series are not detrended (only seasonal cycles are removed). That part of Eq. (1) related to the climate indices has been further removed to the original GMSL time series. The corresponding removed signal is shown  The interannual signal removed from the GMSL by method 1 is highly correlated with the MEI index, with a correlation of 0. 91. The computed regression coefficients are a 3, MEI = 1. 39, a 4, PDO = 0. 11, a 5, IOD = -0. 92, a 6, NAO = 0. 07, and a 7, AMO = 2. 88. We next removed the regressed signal shown above to the initial GMSL. Fig. 6  shows the periodogram of the QD GMSL (CCI/CMEMS dataset) corrected for the regressed signal. Compared to Fig. 2a  (periodogram of the initial QD GMSL), the amplitude of the 3-yr peak has decreased, but the peak is still there.
FIB-SEM datasets were processed to 3D models using open-access software (see Supplementary Fig. 1  and methods for details). This imaging approach allows a wide range of cell volumes to be quantified, from ca. 2 µm 3 in the mamiellophyceae Micromonas, to more than 200 µm 3 in the dinoflagellate Symbiodinium. We observed both external features of microalgae (e. g. the raphe in Phaeodactylum, the flagellum in Micromonas, the coccosphere in Emiliania, Fig. 1 ), and the main organelles (Fig. 2 : nucleus-blue, plastid-green and mitochondria-red). Other cellular features were observed (grey): storage bodies in Emiliania  42, 45  , carbon-rich structures in Pelagomonas  46  , large oil bodies in Nannochloropsis 47 , starch sheaths surrounding the pyrenoids in Micromonas  48  and Symbiodinium, and vacuoles of different sizes in Phaeodactylum 49 , Galdieria and Micromonas 48.
This diversity probably reflects the dynamic nature of these organelles, which frequently change their shape, undergo dislocations, fusion and fission in eukaryotes  51. Quantitative analysis indicates that plastids always occupied the largest fraction (15-40%) of the cell (Fig. 2c , Supplementary Data 1) in line with recent estimates in vascular plants  29, 35  , followed by the nucleus (5-15%) and the mitochondria (2. 5-5%). Altogether, these three organelle types (nuclei, plastids and mitochondria) filled a relatively constant fraction (40-55%) of the total cell volume, despite significant differences in the cell volumes of the different phytoplankton taxa (Symbiodinium e. g. is around 100 times larger than Micromonas). Networks of internal vesicles, the Golgi apparatus, ER, vacuoles and storage compartments (e.
3 ) in the different taxa, to identify possible evolutionarily-preserved morphological characteristics. This analysis was initially biased by the presence of Symbiodinium (Supplementary Fig. 3 ). These dinoflagellate cells, being much larger than the others, led to the clustering of data into two groups (Symbiodinium cells on one side, all the other cells on the other side), preventing the observation of correlation between the other cells. Excluding Symbiodinium from the analysis removed this bias and unveiled the existence of a tight correlation between plastids and mitochondria in terms of volume (the coefficient of determination R 2 , being 0. 95, Fig. 3b ) and surface area ratios (R 2 = 0. 85, Fig. 3c ). No significant correlation was found between the volume/surface ratio of the nucleus and the mitochondria or plastid (R 2 ≤ 0.
Symbiodinium cells were not considered in this analysis, because their size, which largely exceeds the other (Supplementary Fig. 3 ), prevents a meaningful analysis of the volume/surface relationships. Despite the differences in the pyrenoid topology, the ratio of pyrenoid/plastid volumes was preserved in three out of the four microalgae lineages where this compartment was present (7. 1 ± 1. 2%, 9. 3 ± 1. 4%, 7. 2 ± 1. 2% for Phaeodactylum, Emiliania, Micromonas, respectively, Fig. 5a  and Supplementary Dataset 1). This constant ratio highlights the importance of maintaining a proper balance between the subcompartments performing light harvesting (the photosynthetic membranes) and CO 2 fixation (the pyrenoid). An exception to this observation is Symbiodinium, where the pyrenoid occupies a much lower fraction of the plastid volume (2.
Our investigations of light acclimation in Phaeodactylum cells and acclimation to different trophic lifestyles in Nannochloropsis are consistent with this hypothesis, as topological modifications in their cellular engines accompanied physiological changes. These data highlight the intimate links between cellular structures, energy balance and physiological responses. Associating the approach we have developed with cryo-electron tomography, with chemical imaging  74  , or with correlative microscopic studies  75, 76  will vastly improve investigations of phytoplankton as well as vascular plants, e. g. to study the impacts of climate change scenarios  77. It will be critical to assess how changes in temperature and nutrient availability in the oceans affect the subcellular features and acclimation capacity of these key phototrophic microorganisms, so as to predict their future activity at the global scale.
Nannochloropsis growth was measured following changes in the culture optical density at 650 nm. Changes were calibrated with cell numbers in both control and mixotrophic cultures. Photophysiology measurements. Oxygen exchanges were measured with a Clarktype electrode (Hansatech Instruments, UK) at 20 °C, with respiration and gross photosynthesis quantified by measuring the slope in the dark and upon light exposure (intensity 300 µmol photons m -2 s -1 ), respectively. The parameter Fv/Fm (maximum yield of photosystem II photochemistry) 82 was used to compare the photosynthetic capacity of the tested strain with earlier data in the literature, as a proxy for their physiological state. F v /F m was measured with a Speedzen 3 chlorophyll fluorescence imaging setup (Biologic, France). It was calculated as (F m -F 0 )/F m , where F 0 is the minimum fluorescence yield, determined at open photosystem II reactions centres and F m is the maximum fluorescence yield, measured upon closing the photosystem II reaction centres with a short (150 ms) saturating light pulse.
For each slice, a thickness of 8 nm was removed, and the SEM images were recorded with a pixel size of 8 nm, providing an isotropic voxel size of 8 × 8 × 8 nm  3. Whole volumes were imaged with 800-1000 frames, depending on the species. Due to its reduced cell dimensions, the voxel size was reduced to 4 × 4 × 4 nm 3 in the case of Micromonas, resulting in higher resolution datasets with approximately 350-500 frames/cell. Image processing. As a first step of image processing, regions of interest (ROIs) containing cells were cropped from the full image stack. This was followed by image registration (stack alignment), noise reduction, semi-automatic segmentation of the ROIs, 3D reconstruction of microalgae cells and morphometric analysis. Several problems may be encountered during these steps.
Raw stacks consist of big data (50-100 GB for the whole imaged volume, containing several cells) that do not necessarily fit into the computer main memory (RAM). Moreover, cryo-substituted cells generate less contrasted images than cells prepared with chemical fixation. Therefore, the first step in building a robust 3D model consists in 'isolating' a given ROI (e. g. an organelle) from other compartments, to obtain a smaller stack size that can be easily worked with (in practice, we worked with substacks that were around 10% of the original stack size). Single cells were isolated by cropping in three dimensions using the open software Fiji (https://imagej. net/Fiji, Supplementary Fig. 1a ). Image misalignment was corrected using the template matching ("align slices in stack") option implemented in Fiji.
This function finds the most similar image pattern in every slice and translates them to align the landmark pattern across the stack (https:// sites. google. com/site/qingzongtseng/template-matching-ij-plugin) (Supplementary Movies 1-9). Aligned image stacks were filtered to remove noise using Python  83  and OpenCV (https://opencv. org). Filtering techniques were chosen to highlight contours while removing noise in the images. Depending on the species, we found that the osmium staining was not homogeneously distributed. Therefore, it was not possible to filter raw datasets of every species with the same method. Based on the effectiveness in highlighting organelle boundaries, different filters were used for the different microalgae (Supplementary Fig. 1b ). Application of a linear Gaussian filter followed by sharpening to remove noise and enhance contours, which is widely used and easy to implement  84  , was used to process raw datasets of Emiliania, Micromonas, Phaeodactylum and Pelagomonas.
However, this method was less effective when applied to raw datasets of Galdieria and Symbiodinium, where using the median filter proved to be a better de-noising option. These choices reflect the different cellular features and biochemical composition of each taxon (e. g. the presence of a thick cell wall in Galdieria), which results in variable contrast. Segmentation. Segmentation of organelles, vesicular networks, vacuoles and storage compartments was carried out with 3D Slicer software  85  (www. slicer. org, Supplementary Fig. 1c ), using a manually-curated, semi-automatic pixel clustering mode (3 to 10 slices are segmented simultaneously for a given ROI). We assigned colours to the ROIs using paint tools and adjusted the threshold range for image intensity values. The ROIs were annotated and the corresponding label map was run into the model maker module from 3D slicer (Supplementary Fig.
1c ), to generate corresponding 3D models that were exported in different formats (. stl, obj, vtk, ply, mtl). For further analysis, we used the. stl mesh, which proved to be most suitable for 3D analysis in our workflow (Supplementary Table  2 ). 3D reconstructed model. A 3D filtering process was needed to refine the model and reduce the size of the file (see Supplementary Table  2 ). In our case, 3D models generated by 3D Slicer were imported into the open source software MeshLab  86, 87  which automatically removed some 'isolated islands'. Models were further edited manually within MeshLab to eliminate remaining isolated islands erroneously annotated as ROIs. We also performed a remeshing process to facilitate 3D modelling, visualization and animation. Using MeshLab, we simplified meshes ('mesh decimation', Supplementary Fig.
1d ) to reduce the model nodes and faces down to 25% of the original data without modifying morphometric values, such as surfaces and volumes (Supplementary Table  2 ). Every 3D model was imported into Paraview 88 (Supplementary Fig. 1d ) to visualize 3D objects and understand their relationships. Blender (www. blender. org) was used for object animation (Supplementary Movie 10). Morphometric evaluations. Measurement of volumes, surface area, and the minimum distance between meshes) were performed using Numpy-STL (https:// pypi. org/project/numpy-stl/) and TRIMESH (https://trimsh. org/trimesh. html) packages of Python (Supplementary Table  3 ). This Python package is faster than MeshLab, with obvious advantages in terms of analysis of large files (>500 MB). Surface and volume measurements.
Surfaces and volumes were computed using the discrete mesh geometry, with surfaces computed directly from mesh triangles and volumes computed from the signed volume of individual tetrahedrons, assuming a closed surface (watertight mesh, Supplementary Fig. 1e ). Briefly, to compute the surface, we iterated over all the triangles of the mesh. The computation of the cross product between two edges of a given triangle gives a vector whose magnitude is twice the area of said triangle. Then, the sum of all these areas provides the total surface area of the mesh. We then computed the signed volume of all tetrahedrons, which goes from the origin (0,0,0) to each triangle present in the mesh. Assuming a closed surface (watertight mesh), summing all those volumes gives the volume of the mesh  89. A simple implementation of those algorithms is provided in Supplementary dataset 2.
Distance between organelles. Using the Trimesh Python module, the minimal distance between two meshes was calculated based on the closest points between two triangular meshes. Hence, the surface proximity areas were quantified based on: (i) calculating the minimal distance between each vertex of the plastid mesh to the mitochondria mesh (for 3 cells of every species), and then by (ii) gathering mesh vertices according to a given distance threshold to generate proximity surfaces. Two distance thresholds were chosen for this analysis: ≤30 nm, the 'average' one, to define possible contact points  [54] [55] [56]  and ≤90 nm, defining the 'upper limit' for organelle proximity  57, 90. The corresponding surfaces were then compared to the total plastid surface (Supplementary dataset 3).
Acknowledgements The authors thank the  Roscoff Culture Collection  that provided phytoplankton strains and  Noan Le Bescot  ( Ternog Design ) for help in the conception and realization of the figures of this article. This project received funding from the  European Research Council :  ERC Chloro-mito  (grant no. 833184 ) to G. F. , D. F. , G. C. Research was also supported by a  Défi X-Life  grant from  CNRS  to J. D. , funds from the  CEA DRF impulsion FIB-Bio program  to J. D. , P. -H. J. , B. G. , C. M. , D. F. , G. S. ; the  European Union 's  Horizon 2020 research and innovation programme CORBEL  under the grant agreement No  654248  to J. D. , the  LabEx GRAL  ( ANR-10-LABX-49-01 ), financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ) to C.
W. , J. D. , P. -H. J. , B. G. , C. M. , F. C. , G. C. , G. S. , D. F. , G. F. and the  ANR 'Momix'  ( Projet-ANR-17-CE05-0029 ) to G. C. , G. F. This work used the platforms of the Grenoble Instruct-ERIC centre (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL to B. G. , C. M. , G. S. The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation Recherche Medicale (FRM) , the funds  FEDER  and the  GIS-Infrastructures en Biologie Santé et Agronomie (IBiSA)  to B. G. , C. M. , G. S. ; J. D. was supported by  ATIP-Avenir program. C. U. is supported by a joint  UGA-ETH Zurich  PhD grant (to G. F. and S. C. Z. ) in the framework of the " Investissements d'avenir" programme  ( ANR-15-IDEX-02 ).
The implications of biodiversity loss for local soundscape characteristics therefore cannot be directly predicted from count data alone. Here we combine annual systematic bird count data from North American Breeding Bird Survey (NA-BBS) and Pan-European Common Bird Monitoring Scheme (PECBMS) sites with recordings of individual bird species, downloaded from an online database (www. xeno-canto. org), to reconstruct historical soundscapes at over 200,000 locations across the two continents over the past 25 years. Taking the first species listed in a site-year count data file, a 25 s sound file for that species was inserted at a random time point in an initially empty 5 min sound file. Playback volume was randomly sampled from a uniform distribution to represent varying proximity of individual birds to the surveyor.
Acoustic indices have been used to explore diel and annual patterns in soundscape structure  34, 35  and to characterise differences in soundscapes across habitats and landscapes  33, 36, 37. However, evidence of changes in soundscape characteristics over longer time periods is currently lacking because of a scarcity in historical soundscape recordings. By reconstructing soundscapes from large-scale bird monitoring datasets and archived recordings of individual species, both predominantly generated by citizen scientists, we are able to explore changes in soundscape quality at sites across North America and Europe over recent decades. We reveal a chronic deterioration in soundscape quality, defined as a reduction in acoustic diversity and/or intensity, across both continents. Our analyses suggest that changes in the composition, diversity and abundance of bird communities are all likely to have contributed to this.
This approach could also be used to forecast future soundscapes based on projected species' range shifts under environmental change scenarios. However, we strongly advocate for the increased collection and systematic curation of soundscape field recordings from across habitats and environmental gradients to capture all facets of soundscape dynamics, such as changes in anthropogenic noise and vocalisation behaviour across taxonomic groups, not currently integrated into our reconstructions. The rapid increase in autonomous sound recording tools and their widespread use could be harnessed both to launch standardized soundscape monitoring schemes, and to collect soundscape recordings in less structured citizen science databases  55. Such recordings could also be used to derive the vocalisation frequency and duration data needed to further enhance soundscape reconstructions by encoding species-specific insertion criteria in place of the systematic protocol (one individual equals one 25 s sound file) currently applied across all species.
Methods Bird data. North America: we used annual bird count data collated under the North American Breeding Bird Survey (NA-BBS: https://www. pwrc. usgs. gov/bbs/) from 1996 to 2017. NA-BBS survey routes, consisting of 50 survey points (hereafter sites) evenly distributed over ~24. 5 miles, are distributed across the United States and Canada and are usually surveyed in June. At each site, skilled volunteers conduct a three-minute point count, recording all birds seen or heard within a 400-m radius  59. Europe: we used annual bird count data from 23 survey schemes across 22 countries collated under the Pan-European Common Bird Monitoring Scheme (PECBMS: https://pecbms. info) from 1998 to 2018. In each scheme, skilled volunteers carry out either line transects, point counts or territory mapping at survey sites during the breeding season and record all birds encountered  60  (Supplementary Table  5 ); while methods vary between survey schemes, they are consistent within schemes across the time period included here.