dataesr/scientific-paragraphs-categorization huggingface dataset:
Our datasets support this; however, the forest sites tended to also accumulate more snow than the peatland sites (wind erosion is higher on peatlands), which may have contributed to longer snow duration in the forest. Below-canopy measurements of surface fluxes from snow-covered forest floor would be required to investigate the actual contribution of individual energy fluxes to snow melt, but only a few efforts have been made to acquire such datasets in boreal forest environments  (Mazzotti et al. , 2020b; Reid et al. , 2014). Such data was not available for this study, and hence, only energy fluxes above the canopy were evaluated, as is also commonly done  (Napoly et al. , 2020; Essery et al. , 2009; Rutter et al. , 2009).
Limitations and outlook We forced the model simulations with meteorological data from the study sites, and the data gaps were filled with observations from nearby stations and ERA5 reanalysis product  (Hersbach et al. , 2020). Intrinsic uncertainties in meteorological observations are known to exist, especially in northern conditions (instrument freezing, snow blocking, undercatch etc. ,  Stuefer et al. , 2020). The data gaps further add up possible sources of errors. Uncertainties in model forcing can affect model-data comparisons, especially during the gap-filled periods  (Raleigh et al. , 2015). Our EC-based fluxes are among the longest dataset ever used for the evaluation of turbulent flux simulations over snow. The EC-data, however, contains both random and systematic uncertainties (e.
, 2019; Mazzotti et al. , 2019) , yet these datasets are short-term. In particular, below-canopy measurements of turbulent energy exchange are scarce and have to date not been routinely used in snow modeling (e. g. Launiainen, 2010; Launiainen et al. , 2005; Molotch et al. , 2009; Marks et al. , 2008) ). Simultaneous above-and below-canopy measurements may have great potential for snow model evaluations at forest sites. In the absence of energy flux measurements below the canopy, observations of soil temperature and snow conditions allowed an indirect assessment of below-canopy energy budget, and highlighted necessary improvements. In the future, more realistic below-canopy and above-canopy heat flux simulation could be achieved by more sophisticated canopy representations, including multiple layers and species (e.
Acknowledgements. This work was funded by the  Academy of Finland (ArcI Profi 4). GM was funded by the  Swiss National Science Foundation  (Grant  P500PN_202741 ). SL acknowledges  GreenFeedBack  project from the  EU   Horizon Europe -Framework Programme for Research and Innovation  (no. 101056921 ). PA was funded by the  Academy of Finland   Research Fellow grant  (no. 347348 ). PK, MA and AL thank the support from the  ACCC Flagship  funded by the  Academy of Finland  (no. 337549  and no. 337552 ) and  ICOS-Finland  by  University of Helsinki  and the  Ministry of Transport and Communication. MA and AL also acknowledge the  Academy of Finland (UPFORMET , grant no. 308511 ). The authors would like to thank  Bertrand Decharme ,  Christine Delire ,  Bertrand Bonan  at  CNRM/GAME  and  Marie Dumont  at  CNRM/CEN  for their great support during this work.
Code availability. The SURFEX is an open source project (http://www. umr-cnrm. fr/surfex) but it requires registration. The full procedure and instructions are available at https://opensource. umr-cnrm. fr/projects/snowtools_git/wiki/Procedure_for_new_users. The SURFEX version used in this work is available in git (tagged as boreal_ecosystems). The ESCROC version developed for external SURFEX users, is available at https://github. com/bertrandcz/CrocO_toolbox.
https://doi. org/10. 5194/egusphere-2023-338 Preprint. Discussion started: 8 March 2023 c Author(s) 2023. CC BY 4. 0 License. teorological Institute (FMI) open database (FMI, 2021) (Station IDs: N-WET 778135, N-FOR 101317, S-FOR 101987). Meteorological observations at the S-WET site come from the SMEAR database.
For this purpose, reference evaluation datasets should be complemented with more boreal and Arctic sites and observations of all components of surface energy balances, particularly turbulent fluxes. Such a dataset would facilitate similar experiments with other models. With well-selected model configuration and parameterization, SURFEX model platform can realistically simulate surface energy fluxes and snow and soil conditions in the subarctic and boreal peatlands and forests. The common version of ISBA (ISBA-VS) can provide rather realistic lower boundary conditions for numerical weather prediction (NWP) and global circulation models (GCMs), in expense of non-realistic predictions of forest snow and soil conditions necessary for hydrological applications. We expect that the future inclusion of MEB in operational systems will reconcile these applications by improving the skill of weather forecasts, climate scenario simulations and hydrological forecasts.
, 2018). For this study, we use the version 5. 3 of the SAGE III/ISS level 2 solar aerosol product. The profiles are monthly averaged in four latitude bands of width ∆φ = 20°, using data from November 2021 to June 2023. Due to its sparse geographical sampling, SAGE III began to see the HTHH SA plume only in March 2022 limiting our approach to the months beyond March 2022. During January and February 2022, SAGE III seldom sampled the plume and data are used for comparison between the plume and background conditions.
Retrieval of Aerosol Size Parameters at 3 Wavelengths We use a method implemented by  Wrana et al. (2021)  to retrieve the SA size distribution which is based on the two color ratios 449/755 nm and 1,543/755 nm for the aerosol extinction coefficients. These ratios are modeled for spherical liquid SA through Mie calculations using miepython, a python code where the Mie theory is implemented following the procedure described by  Wiscombe (1979). We use fixed refractive indices from the GEISA spectroscopic database  (Armante et al. , 2016) , for a temperature of 215 K and a H 2 SO 4 weight proportion w s of 70 % considering that the rest of the liquid droplet is water  (Biermann et al. , 2000) , that is {n 449 , n 755 , n 1543 } = {1. 439, 1. 438, 1. 42}. This weight proportion has been obtained according to  Tabazadeh et al.
Open Research Section SAGE III/ISS L2 Solar Event Species Profiles V053 data set is available from https:// doi. org/10. 5067/ISS/SAGEIII/SOLAR NetCDF4 L2-V5. 3. miepython is a free package available from https://miepython. readthedocs. io/en/latest/ and https://doi. org/ 10. 5281/zenodo. 8023972. We used version 2. 3. 0 in this study. AERIS has provided access to the GEISA database at https://geisa. aeris-data. fr.
Acknowledgments This study has been supported by the  Agence Nationale de la Recherche  under Grant  21-CE01-0007-01  ( ANR   ASTuS ) and the  Centre National d' Études Spatiales (CNES). Work at the  University of Greifswald  was funded by the  Deutsche Forschungsgemeinschaft (DFG Research Unit VolImpact , Grant  398006378 ).
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods.
The Cell Tracking Challenge (CTC) (http://celltrackingchallenge. net) is an ongoing initiative that promotes the development and objective evaluation of automated cell tracking algorithms. Launched in 2013 under the auspices of the 10th IEEE (Institute of Electrical and Electronics Engineers) International Symposium on Biomedical Imaging (ISBI), the CTC provides developers with a rich and diverse annotated dataset Analysis https://doi. org/10. 1038/s41592-023-01879-y a selected part of the embryo is covered. The silver truth consists of computer-generated segmentation annotations obtained by fusing the results of high-performing benchmarked methods over the training sequences, using the detection gold truth to drive the fusion process (see 'Silver standard reference annotation' in Methods).
This silver truth improves the cell instance coverage (99. 1% on average), providing the participants with a larger set of annotated cell instances that can be used, for example, to train deep learning models. Supplementary Data Tabs 1 and 2 contain the coverage of both gold truth and silver truth annotations for all datasets. Note that 100% coverage was not attainable because even the best-performing benchmarked methods did not always detect and segment all cell instances in a particular video. All reference annotations are publicly available for the training datasets but are kept secret for the test datasets. This helps prevent overfitting by not enabling the methods to be tuned specifically for the test data. Thus, it ensures that the performance of the methods is evaluated based on their ability to generalize rather than their ability to memorize the training data.
Participants, algorithms and handling of submissions The CTC has witnessed a remarkable increase in participation since the time of our last report in 2017 (ref. 9). The number of participating teams has increased from 16 to 50, representing 19 countries. The number of benchmarked algorithms has also increased from 21 to 89. All submissions, consisting of labeled segmentation masks and structured text files with cell-lineage graphs in the case of tracking results, followed standardized naming conventions and were verified by the CTC organizers using the provided executable versions of the algorithms. A complete list of the participants' segmentation and tracking algorithms can be found in Supplementary Data Tabs 3 and 4, and a global overview of the strategies and techniques used is presented in Fig.
Technical performance of the submitted algorithms The CTC has two benchmarks: the Cell Tracking Benchmark (CTB) and the Cell Segmentation Benchmark (CSB). The CTB, which has been active since the inception of the CTC, evaluates the segmentation (SEG) and tracking (TRA) accuracy of the submitted methods. The CSB, introduced in 2019, focuses on segmentation (SEG) and detection (DET) accuracy, without considering the linking of cells over time. All these evaluation measures are described in Methods (in the 'Quantitative performance criteria' section). The scores of all CSB and CTB submissions received before 1 June 2022 can be found in Supplementary Data Tabs 5-9 (CSB) and Tabs 10-14 (CTB). Figure  4a  shows the SEG and DET performance of the top-3 CSB methods, along with the overall performance measure (OP CSB ), calculated as the arithmetic mean of both measures.
repository of multidimensional time-lapse microscopy videos along with objective measures and procedures to evaluate their algorithms. These highly valuable resources are freely available to the scientific community for use in their research. In 2014 the first report was published  8  , describing the CTC submission and evaluation procedures and presenting the analysis of the results submitted by six participants for a repository containing eight datasets. In 2017 an in-depth analysis of 21 algorithms was published  9  , based on the segmentation and tracking results submitted for 13 datasets. From the results presented, we concluded that the methods that used contextual (that is, spatial and temporal) information and those few at the time that followed learning strategies outperformed the more conventional methods.
Notably, the state-of-the-art U-Net 10 architecture was among the top-performing approaches for cell segmentation in several contrast-enhanced datasets. It was also notable that completely unsupervised tracking methods were still a distant dream. The optimal solutions remained dataset specific due to the complexity and diversity of the datasets. Moreover, most proposed methods were still inadequate for low signal-to-noise ratio videos or for tracking cells with complex shapes or textures. Large three-dimensional (3D) datasets, such as those of developing embryos, were identified as extremely challenging due to the high number and density of cells, as well as the computational requirements of their processing. Since 2017, the CTC has received a significant number of new submissions and has addressed many of the challenges previously identified in the field of automated cell tracking, as described in the following sections.
Datasets The CTC dataset repository has been extended from 13 datasets in 2017 to 20 datasets. The new datasets consist of twodimensional (2D) epi-fluorescence time-lapse videos of human hepatocarcinoma-derived cells expressing a fusion yellow fluorescent protein (YFP)-TIA-1 protein (Fig. 1a ); 2D bright-field time-lapse videos of mouse hematopoietic (Fig. 1b ) or muscle (Fig. 1c ) stem cells in hydrogel microwells; 3D time-lapse videos of green fluorescent protein (GFP)-actin A549 lung cancer cells (Fig. 1d ) and their computer-generated counterparts (Fig. 1e ) displaying prominent, highly dynamic filopodial protrusions; and mesoscopic videos (imaged across several millimeters at video frame rate) of developing Tribolium castaneum embryos available as 3D cartographic projections (>10 GB per sequence) (Fig.
Reference annotations The CTC provides two measure-specific reference annotations: segmentation annotations consisting of cell instance masks, which outline individual cell regions, and tracking annotations consisting of cell markers interlinked between frames to form lineage trees. The reference annotations can be classified into three types based on their source and how they were generated. For synthetic datasets, generated using in-house developed software  11, 12  , the segmentation and detection and/or tracking reference annotations are the exact, simulated digital cell phantoms prior to the addition of distorting noise and blur. For real datasets, we distinguish between a gold standard reference corpus (in short, gold truth) and a novel silver standard reference corpus (in short, silver truth).
The gold truth is obtained by taking a majority opinion between three experts. The segmentation gold truth offers limited cell instance coverage (17. 8% on average; Supplementary Data Tabs 1 and 2) due to the labor-intensive nature of manual annotations. The detection and tracking gold truth offers complete cell instance coverage, except in large embryonic datasets, where only Analysis https://doi. org/10. 1038/s41592-023-01879-y Globally, the evolution of the CSB scores obtained by the best-performing methods from 2017 to June 2022 is given in Extended Data Fig. 1a , which shows clear improvement in both detection and segmentation on most datasets, with particularly impressive improvements on two of the most complex datasets (Fluo-C2DL-MSC and Fluo-N3DL-DRO). In summary, even if the cell detection task seems nearly solved for most datasets, the segmentation task still requires further attention for some of the old (Fluo-C2DL-MSC, Fluo-C3DL-MDA231, Fluo-N3DL-DRO and PhC-C2DL-PSC) and new datasets.
Image quality versus algorithm performance We analyzed the relationship between the technical performance values obtained by the participants (Supplementary Data Tabs 5-9 and Tabs 10-14) and the quality of the datasets listed in Fig. 2. As described in the 'Statistical analysis' section of Methods, we calculated the Spearman's rank correlation between each numerical quality measure of the dataset and the performance of all competing algorithms. The analysis was conducted globally, that is, considering all datasets, per data modality, and individually. The results are presented in Supplementary Figs. 1-40. Globally, we discovered that only the cell overlap (Ove) showed correlation (moderate, rho = 0. 4) with the segmentation performance (SEG) of the algorithms (Supplementary Fig. 34 ). This result, besides pointing at other cofactors that may work along with Ove, indicates that the cells that do not dramatically change their shape, or show a moderate motility, are easier to segment than those with high shape variability or high motility.
Looking at the correlations per modality, strong correlations were found between the performance of the methods on Fluo-2D datasets and the signal-to-noise ratio (SNR; positive for TRA, Supplementary Fig. 4 ), resolution (Res; negative for TRA, Supplementary Fig. 20 ), shape (Sha; positive for SEG, Supplementary Fig. 22 ) and mitotic division rate (Mit; positive for TRA, Supplementary Fig. 40 ). The positive effect of high SNR and regular cell shape (high Sha) could be expected. The counterintuitive benefit of a low Res for tracking can be explained by the negative effect of the low performance values obtained for two complex datasets, Fluo-C2DL-MSC and Fluo-C2DL-Huh7, which have relatively high Res levels but which are plagued by irregular cell shape (low Sha), high photobleaching (high change in cell signal intensity over time, that is, high Cha), low SNR and low contrast ratio (CR) (Fluo-C2DL-MSC), high levels of signal heterogeneity (both inside and between cells, that is, Het i and Het b , respectively) and low Mit (both Fluo-C2DL-MSC and Fluo-C2DL-Huh7).
These negative factors clearly outweigh the benefits of their relatively high Res. Regarding the Fluo-3D datasets, the high differences between datasets is reflected in the moderate correlations between the performance of the methods and Res (positive for SEG and TRA, Supplementary Figs. 18  and 20 ), Sha (negative for SEG, Supplementary Fig. 22 ), and the spacing between cells (Spa; positive for SEG Supplementary Fig. 26 ). Bright-field performance values correlate with SNR (positive for SEG and TRA, Supplementary Figs. 2  and 4 ), CR (negative for SEG and TRA, Supplementary Figs. 6  and 8 ), Het i (negative for SEG and TRA, Supplementary Figs. 10  and 12 ), Het b (negative for SEG and TRA, Supplementary Figs. 14  and 16 ), Res (negative for SEG, Supplementary.
Analysis https://doi. org/10. 1038/s41592-023-01879-y Fig. 18 ), Sha (positive for SEG, Supplementary Fig. 22 ), and Spa (negative for SEG, Supplementary Fig. 26 ). Most of these correlations could be expected, except for the counterintuitive effect of Res and Spa. This could be explained by the fact that in one of the two bright-field datasets (BF-C2DL-HSC), the negative effect of the low Res and Spa, compared with the other dataset, BF-C2DL-MuSC, seems to be lower than the benefits of a lower Het b , uniform shape (high Sha) and high Ove. Regarding the phase contrast (PhC) datasets, the performance values correlate with CR (negative for SEG and TRA, Supplementary Figs. 6  and 8 ), Het i (positive for SEG and TRA, Supplementary Figs. 10  and 12 ), Het b (positive for SEG and TRA, Supplementary Figs.
14  and 16 ), Res (positive for SEG and TRA, Supplementary Figs. 18  and 20 ), Sha (negative for TRA, Supplementary Fig. 24 ), Spa (positive for SEG and TRA, Supplementary Figs. 26  and 28 ) and Cha (negative for SEG and TRA, Supplementary Figs. 30  and 32 ). These results are heavily influenced by the fact that the two types of phase contrast datasets available have strikingly different characteristics. This explains, for instance, the unexpected negative correlation found with CR, given that the dataset with higher CR values (PhC-C2DL-PSC) is more complex to analyze than PhC-C2DH-U373, due to the negative impact of other factors, most notably a significantly lower Res, Spa and higher Mit. Interestingly, the levels of heterogeneity (both Het i and Het b ), positively correlate with the performance of the methods for this modality, suggesting that the characteristic complex texture and halo-like artifacts of phase contrast images are beneficial for methods that are based on the recognition of patterns, as is the case for machine learning methods.
Finally, no correlations were found for the only existing differential interference contrast (DIC) dataset, as could be expected due to the low number of elements (n = 2 videos) available for the analysis. Beyond these global and modality-specific results, other relevant observations (outside the scope and length of this paper) relating to the properties that affect the segmentation and tracking performances can be obtained from the distributions shown per dataset in Supplementary Figs. 1-40.
We next looked at the correlation between the quality of the reference annotation parameters listed in Fig. 5 , and the performance of the competing submitted algorithms (Supplementary Data Tabs 5-9 and Tabs 10-14). The complete set of results can be found in Supplementary Figs. 41   that what is difficult for humans to do is also difficult for automated algorithms to solve. In the context of segmentation, there exists room for more consistent annotation, as indicated by the MSEG GT quality scores (Fig. 5 ). Therefore, increasing the consistency of the annotations should improve algorithm performance. Our per-modality look at the correlations confirm the global trend with different levels of strength, except for DIC, which could be partly due to the low number of datasets of this modality.
Regarding the quality of the silver truth annotations, a strong or moderate global correlation of the quality parameters with SEG ST and DET ST was found (Supplementary Figs. 48  and 50 ). This is also expected given that the silver truth annotations were obtained as a combination of the best-performing methods, resulting in almost fully annotated datasets. Finally, modality-based and individual deviations from this rule were also found, in most cases due to a low number of datasets of the modality. N N N N N N N N N N Y N N Y Y N N N N N N N Y Y Y N Y Y Y Y N Y N N N Y Y N Y Y N N Y N N N N N N Y N Y N N N N N N N N N N Y Y N N N N Y Y N N N N Y Y Y N N N BF-C2DL-MuSC DIC-C2DH-HeLa Fluo-C2DL-Huh7 Fluo-C2DL-MSC Fluo-C3DH-A549 Fluo-C3DH-H157 Fluo-C3DL-MDA231 Fluo-N2DH-GOWT1 Fluo-N2DL-HeLa Fluo-N3DH-CE Fluo-N3DH-CHO Fluo-N3DL-DRO Fluo-N3DL-TRIC Fluo-N3DL-TRIF PhC-C2DH-U373 PhC-C2DL-PSC Fluo-C3DH-A549-SIM Fluo-N2DH-SIM+ Fluo-N3DH-SIM+.
Evolution of the segmentation and tracking paradigms We analyzed how different segmentation and tracking strategies (Fig. 3a ), as well as individual detection, segmentation and linking techniques (Fig. 3b ), relate to the technical performance of the benchmarked algorithms. Our analysis shows that the DetSeg strategies significantly outperform the Seg strategies for the datasets with heavily clustered cells such as DIC-C2DH-HeLa (Extended Data Fig. 2b ). Indeed, the machine learning-based detection of individual cells turns out to be a crucial factor that reduces the number of under-segmentation and over-segmentation errors penalized by DET, as first demonstrated by MU-CZ (2) and MPI-GE (CBG) (1) in two dimensions and three dimensions, respectively, in 2019 (Extended Data Fig. 3  and Supplementary Data Tabs 3 and 4).