dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the developed workflow must be shared as code (for example, via code repositories such as https://github. com/) or pipelines (for example, KNIME workflow, CellProfiler pipeline). Example input and output and any manually generated inputs (that is, ROI) must be made available (Image availability). For new workflows that were created using software that does not allow scripting, the workflow steps must be carefully described with text. Recommended. All settings of the workflow must be disclosed and described to help reproduction of the analysis. Example input, output and manual inputs (ROI) should be provided via public repositories such as Zenodo. The developer should describe the rationale as well as the limitations of the workflow and the used components in more detail in the methods or supplementary material.
Machine learning workflows Machine learning and especially deep learning have recently become capable of surpassing the quality of results of even the most sophisticated conventional algorithms and workflows and are continuing to advance  67. Deep learning procedures are quickly adapted to microscopy image tasks such as U-Net 68 for cell segmentation  69  , Noise2Void for image reconstruction  47  , StarDist  70, 71  and Cellpose 62 for instance segmentation, DeepProfiler  72  for feature extraction and Piximi (https://www. piximi. app/) for image classification. In machine learning workflows (supervised, unsupervised, self-supervised, shallow or deep learning), input image data are transformed by one or multiple distinct mathematical operations into a scientific result. The instructions for this transformation are learned from provided data (for example, labeled data for supervised learning and unlabeled data for unsupervised learning) to produce a machine learning model.
However, the precise makeup of this model is not easily accessible to a user and depends strongly on the quality and nature of the supplied training data as well as on the specific training parameters. Biases in the training data and errors in the labels of ground truth for supervised machine learning will bias machine learning models  [73] [74] [75]. Reporting is thus even more critical for reproducibility and understandability when machine learning applications are applied for image analysis. Three major approaches are widely used in machine learning-based image analysis today that require different documentation. (1) Pretrained models are directly applied to new image data, and referral to existing references is sufficient. (2) Pretrained models are retrained (transfer learning) with new image data to improve the application, and, in this case, more information must be provided.
Recommended (retrained and new models). To facilitate the reproduction and validation of results from either models trained from scratch or pretrained models that were retrained, the full training and testing data and any training metadata (for example, training time) should be made available. The code used for training the model should be provided. Code as well as data should be provided via public repositories (for example, Zenodo, GitHub). The authors should discuss and ideally test how well the model has performed and show any limitations of the used machine learning approach on their data. The application of machine learning models will particularly benefit from being deployed in a cloud-hosted format or via software containers.
On the contrary, transparent requirements for publishing images and progress-monitoring checklists will ease the path from starting a microscopy experiment to producing reproducible  76  , understandable image figures for all scientists. Recommendations extending the 'minimal' level are introduced in the 'recommended' and 'ideal' reporting levels and at times go beyond what is easy to implement with standard tools today. They are meant to encourage continuous striving toward higher-quality standards in image publishing. Before all these advanced standards can become a new norm, technologies, software and research infrastructure must still be improved. At present, no image database is used widely enough to become a go-to solution, although dedicated resources exist and are slowly gaining traction and publishers are experimenting with parallel solutions (for example, EMBO source data).
Also, while funding agencies increasingly require data to be deposited in repositories, few guidelines are provided for publishing terabytes to petabytes of raw data. While publishers may mandate data deposition or availability, they are not always reviewing its implementation. Combined with a lack of recognition of efforts put into publishing original image data, scientists are often discouraged to make data openly available. Commercial solutions for data storage are becoming increasingly available. For instance, Amazon Web Services Open Data has already been used to host image data (https://registry. opendata. aws/cellpainting-gallery/), and we believe that, ultimately, images presented in most publications should be linked to images processed by lossless compression that are amenable to reanalysis.
Acknowledgements We acknowledge the support of and discussions with all our colleagues and QUAREP-LiMi Working Group 12 members. We additionally thank  O. Biehlmeier ,  M. Bornhäuser ,  O. Burri ,  C. Jacobs ,  A. Laude ,  K. Ho  and  R. D'Antuono  for further feedback and discussions on the manuscript and their endorsement of the checklists. Icons were designed by the authors and are from https://fontawesome. com; ImageJ sample images are from refs. 79-81. C. S. was supported by a grant from the  Chan Zuckerberg Initiative napari Plugin Foundation  ( 2021-24038 ). C. B. was supported by grants from  ANID  ( PIA ACT192015 ,  Fondecyt 1210872 , Fondequip  EMQ210101 ,  Fondequip EQM210020 ) and  PUENTE-2022-13  from  Pontificia Universidad Católica de Chile. B. A. C. was funded by  NIH   P41 GM135019  and grant  2020-225720  from the  Chan Zuckerberg Initiative DAF , an advised fund of the  Silicon Valley Community Foundation.
F. J. was supported by  AI4Life (European Unions 's  Horizon Europe research and innovation program ;  101057970 ) and by the  Chan Zuckerberg Initiative napari Plugin Foundation  ( 2021-240383  and  2021-239867 ). M. Hammer and D. G. were supported by  NSF  award  1917206  and  NIH  award  U01 CA200059. R. N. was supported by grant  NI 451/10-1  from the  German Research Foundation  and grant  03TN0047B  ' FluMiKal ' from the  German Federal Ministry for Economic Affairs and Climate Action. A. P. -D. was supported by  EPSRC  grant  EP/W024063/1. C. S. -D. -C. was supported by grant  2019-198155  (5022) awarded by the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation , as part of their  Imaging Scientist Program. She was also funded by  NIH  grant  U01 CA200059.
Recommended Pseudocolored images: additionally provide grayscale version for comparison Ideal Gamma adjustments: additionally provide linear-adjusted image for comparison Image annotation Add scale information (scale bar, image length in figure/figure legend) Minimal Explain all annotations (in figure/figure legend) Annotations should be legible (line width, size/point size, color) Annotations should not obscure key data Annotate imaging details important for interpreting the figure (depending on the main message and imaging technique, this may be, e. g. , image pixel size, imaging intervals (time-lapse in movies), exposure Recommended time or anatomical section) Image availability Images are shared (lossless compression/microscope images) Image files are freely downloadable (public database) Recommended Image files are in dedicated image database (added-value database or image archive) Ideal.
Patients We included patients admitted to ICU in Anesthesiology and Intensive Care Department (Hôpital E. Herriot, Hospices Civils de Lyon) with septic shock. These patients belonged to the IMMUNOSEPSIS study registered at clinicaltrials. gov (NCT04067674). EDTA blood samples were obtained at day (D): D1-2, D3-4 and D6-8 (one sample per period). Because of the non interventional nature of the study, written informed consent was not required for inclusion. However, each clinician orally informed the patient or family members about the objectives, methodology, and conduct of the study, and provided them with a lea et using plain language. Non opposition to participation to this study was systematically obtained from the patient or a third party before any blood sampling was performed and was recorded in patient's clinical le.
All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee (Comité de Protection des Personnes Ouest II, IRB number #19. 01. 23. 71957) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Clinical and biological databases were reported to the National Commission for Information Technology and Freedom (CNIL, number 08-27). A written non-opposition to the use of donated blood for research purposes was obtained from healthy volunteers (n = 50). Usual clinical data of patients such as age, sex, severity scores, exposure to invasive device, primary sit of infection, nosocomial infection appearance, length of stay in ICU and mortality on day 28 were collected. Nu. Q H3. 1® ImmunoAssay Nucleosomes were measured using Nu.
In this context, elevated nucleosomes may indicate a treatable trait. Clinical development of inhibitors to directly target NET formation, has already been started  [21, 28, 38, 39]. In addition, therapeutic plasmapheresis (i. e. , the selective extra corporeal removal of NET) is under investigation (NCT04749238). In case of favorable preliminary results, nuclesosome levels would constitute an obvious candidate to guide individualized therapy as companion biomarker. This study has some limitations that need to be addressed. Firstly, the cohort used in the study consisted of critically ill septic shock patients with a high mortality rate. Therefore, the results of the study cannot be generalized to all septic patients. Secondly, this study was conducted retrospectively using the IMMUNOSEPSIS cohort, which was primarily aimed at monitoring immunosuppression (NCT02803346).
Acknowledgements The authors would like to thank (1) the clinical research assistants for their great help in the screening and sampling of patients as well as in the preparation of case report forms, (2) the technical staff from " Laboratoire de biologie médicale et d'anatomie pathologique multi sites" (Hospices Civils de Lyon, F)  for their dedicated involvement in samples processing. The authors would also like to acknowledge the important contribution and motivation of the medical and paramedical staff of the  Anesthesia and Critical Care Medicine Department of the Edouard Herriot Hospital.
As one of the most important ice core records, the stable water isotopic record is used to reconstruct past temperatures and provides a fundamental understanding on past climate change  (Brook and Buizert, 2018; Buizert et al. , 2021; Jouzel et al. , 2007). Conventional temperature reconstructions by stable water isotopic records in ice cores rely on the empirical spatial linear relationship between the isotopic composition in surface snow and air temperature. The establishment of such relationship requires sufficient observational data. Despite the large number of isotopic observations made in Antarctica over the past few decades, the spatial coverage is still uneven. Masson-Delmotte et al. (2008, hereafter MD08)  compiled the first multi-year-averaged Antarctic surface snow stable isotope dataset, which provided a solid foundation for related research on Antarctic water isotope climatology.
First, it provides an observational basis for numeric simulations of the spatial distribution of snow isotopes across Antarctica using pure mathematical methods combined with high-resolution digital elevation models  (Hatvani et al. , 2017; Wang et al. , 2009a Wang et al. , , 2009b Wang et al. , , 2010)). Second, it can be used to reconstruct paleo-temperature and paleo-elevation changes  (Werner et al. , 2018). Finally, the database can be used as a benchmark to evaluate isotope-enabled atmospheric general circulation models (AGCMs) and Rayleigh distillation isotope models. relative to the MD08. This article aims to provide some details on the collection of the isotopic measurements used to produce this updated Antarctic surface snow isotopic dataset, including data sources, data spatial distribution, and data selection criteria.
Description of the Antarctic surface snow isotopic dataset 2. 1 Data collections and sources The Antarctic surface snow isotopic data were collected from published papers and public data portals. When the raw data used in the published studies were not publicly available, we requested the data from the authors. We received strong support in the process of data collection. Figure  1a  shows the data points in the original MD08 dataset. Figure  1b  shows our newly added observation points (794 new data points), and Figure  1c  shows the total sampling points (1867 data locations) of our updated dataset. Traverse sampling is an important way to obtain spatially distributed data of isotopic composition. Therefore, the new data mainly came from different route traverses (Fig. 2 ). In particular, over the Dronning Maud Land (DML) (from 0 to 60°E), we compiled the water isotope data from the Swedish-Japanese traverse between Syowa Station and Dome F Station  (Touzeau et al.
5 Spanish expedition from Novolazareskaja Station to South Pole Station  (Landais et al. , 2017). Our dataset also included published records from the Chinese traverse from Zhongshan Station to Dome A, near the Lambert Glacier  (Li et al. , 2014 (Li et al. , , 2021)). For the high inland region of East Antarctica (90 to 120°E), we added two routes of traverse data (Vostok-Dome B and Vostok-Dome C traverses). Furthermore, in the area around the Vostok site, we included 89 unreleased public snow pits data, such as the Vostok flow line data  (Ekaykin et al. , 2012). Previous data over the West Antarctica Ice Sheet (WAIS) area mainly came from the International Trans-Antarctica Scientific Expeditions  (Qin et al. , 1994; Steig et al. , 2005). The updated dataset includes more recent traverses of the WAIS by Brazilian and Chilean researchers from the Möller Ice Stream (MIS) basin to the Pine Island Glacier (PIG) basin  (Marcher et al.
Selection criteria and types of data collected in the Antarctic surface snow isotopic dataset We applied strict data selection criteria to ensure consistent quality over the entire dataset. As we aimed for a reliable isotopic dataset of multi-year averages, we excluded isotopic data with apparent seasonal bias and those modified by post-depositional processes. For example, previous studies reported a high number of surface snow samples with negative d-excess values in the Dry Valleys  (Gooseff et al. , 2006; MD08; Hu et al. , 2022). They likely came from fresh surface snow or surface glacial ice, which could not represent the multi-year average isotope values. Therefore, we excluded these surface samples from the Dry Valleys  (Gooseff et al. , 2006; MD08) , and only retained data from snow pits.
Metadata Table 1 provides a detailed description of metadata fields used in our Antarctic surface snow isotopic observation dataset. Key information included the site latitude and longitude, geographic factors (elevation and distance to the nearest coast), climate conditions (temperature and net snow accumulation rate), and isotopic values. Latitude and longitude data were provided in the literature or by contributing researchers. Elevations were extracted from the Global Earth Relief Grids data (https://lpdaac. usgs. gov/products/srtmgl3v003/) at a spatial resolution of 0. 09 km. We applied the built-in functions in the Generic Mapping Tools (GMT) software  (Wessel et al. , 2019)  to calculate distance from the nearest coast. Firn temperature or surface air temperature data were obtained either from the literature or provided by the contributing researchers.
The net snow accumulation rate, when available, was expressed in cm w. e. yr -1. The dataset contains 1867 data points in total, including 1604 locations for δ 18 O and 1278 locations for δ 2 H, and 1204 positions for both, where the d-excess (defined as d-excess = δ 2 H -8 × δ 18 O) could be calculated. (1) analytical uncertainty of 0. 1‰ or better for  In order to compare our isotopic dataset with the simulations from isotope-incorporated AGCMs, we used the published results of two recent versions of isotope-enabled AGCMs: the European Centre for Medium-Range Weather Forecasting-Hamburg Atmosphere Model equipped with water isotopes, version 6 (ECHAM6-wiso) and the isotope-enabled Community Atmosphere Model, version 6 (iCAM6). The ECHAM6-wiso model was driven by the ERA5 reanalysis dataset  (Hersbach et al.
Conclusions and outlook We compiled a multi-year-averaged Antarctic surface snow isotopic dataset by integrating a previous database with more recent observations. This dataset greatly improved the spatial coverage of isotopic observations in Antarctica. As an example of its potential applications, we made a comparison between the isotopic observation data and the simulation results from the two most recent isotope-enabled AGCMs. The results show that the ECHAM6-wiso and iCAM6 in general captured the spatial For better data integration and update, we make the following recommendations for future studies: (1) more data are needed at high elevations in interior Antarctica;  (2)  data collection techniques such as dual-tube sampling are needed to ensure annual data coverage and reduce seasonal biases; (3) all isotopic observation data should be made publicly available.
Acknowledgements. This work was jointly supported by the  National Natural Science Foundation of China  ( 41622605 ,  41830644 , and  91837102 ), the  Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) , the  Collaborative Innovation Center of Climate Change of Jiangsu Province of China , the  Fundamental Research Funds for the Central Universities  ( 020914380190 ), and the  ERC Starting Grant COMBINISO  ( 306045 ). We thank  Jean-Louis Tison  for his support and  Jesse Nusbaumer  for the interpretation of the iCAM6 results. We are grateful for the help of https://doi. org/10. 5194/essd-2022-384 Preprint. Discussion started: 30 November 2022 c Author(s) 2022. CC BY 4. 0 License. 1  Jean Jouzel  and  Jiandong Xu. Finally , we thank the scientists who have provided help and support in data collection.
Although isotopic observations have been recorded at over one thousand sampling sites, the spatial coverage of isotope data remains highly uneven (MD08). Only a few data are available on the West Antarctic Ice Sheet and at high altitudes of the East Antarctic interior regions. After the pioneering work of MD08, numerous new https://doi. org/10. 5194/essd-2022-384 Preprint. Discussion started: 30 November 2022 c Author(s) 2022. CC BY 4. 0 License. 4 samples and measurements have been acquired by different researchers. Incorporating these additional observations, we have recompiled the most comprehensive Antarctic surface snow isotopic dataset, which significantly increases the spatial coverage.
variation and characteristics of Antarctic precipitation isotopes. The updated dataset has many important potential applications in investigating spatial variability and the climatology of water isotopes in the region, evaluating models, and interpreting Antarctic ice core records for past climate variations. https://doi. org/10. 5194/essd-2022-384 Preprint. Discussion started: 30 November 2022 c Author(s) 2022. CC BY 4. 0 License. 1.
Data Collection and Pre-Processing We updated the original MAREDAT pteropod and foraminifer abundance and biomass data sets of  Schiebel and Movellan (2012)  and  Bednaršek, Mozina, et al. (2012)  by aggregating abundance concentration data from large scale sampling campaigns, existing data compilation efforts, and unpublished sampling data (Figure  1 ). The main data sources (Figure  S1  in Supporting Information S1) for both plankton groups included the Southern Ocean Continuous Plankton Recorder (SO-CPR; Hosie, 2021), the Australian CPR (Aus-CPR; IMOS, 2022), the North Atlantic and North Pacific CPR (NA-NP CPR;  Johns, 2021) , and the Coastal and Oceanic Plankton Ecology, Production and Observation Database (COPEPOD; T. D. O'Brien, 2010). For pteropods, we added data from the Tara Oceans expeditions  (Brandão et al.
The final, quality-controlled pteropod abundance data set (Figure  S2  in Supporting Information S1) contains 841,239 data points at 309,921 individual locations, collected at a mean sampling depth (±sd) of  38. 15 ± 190. 89 m over the 1938-2021 period (2001. 25 ± 15. 23). Abundances range between 0 ind/m 3 and 1,066. 67 ind/m 3 , with a mean of 4. 38 ± 79. 86 ind/m 3. The median abundance (0. 00 ind/m 3 ) is low due to the CPR data sets which make up 91. 15% of the data, and contain 92. 06% absence observations. 50. 19% of the data is resolved only to the order-level, whereas 24. 03% of the observations are species-resolved and 22. 41% resolved to the genus level (see Table  S2  in Supporting Information S1). The data set contains observations on 33 species out of 165 currently recognized pteropod species  (Peijnenburg et al.
00 ind /m 3. 59. 79% of the data are species resolved, followed by 33. 07% of the observations on a phylum level (see Table  S4  in Supporting Information S1). This data set contains observations on 42 of the 47 extant foraminifer species  (Schiebel & Hemleben, 2017). Most of the total abundance is composed of Globigerina bulloides (25. 6% of the total species-resolved abundance), Neogloboquadrina incompta (23. 7%), Turborotalita quinqueloba (13. 3%), and Globigerinita glutinata (11. 3%). For model training, we performed additional data quality controls to ensure sensible relations between environmental predictors and biomass values could be derived. The NA-NP CPR data set was flagged and discarded for modeling as it contained discrete medians of abundance bins instead of continuous values (removal of 340,250 points for pteropods and 250,620 points for foraminifers).
Surface Ocean Aggregation To reduce spatio-temporal patchiness and noise in the data, we conducted a surface ocean aggregation (C. J. O'Brien, 2015). To this end, we re-gridded all data onto the 1 × 1° grid of the World Ocean Atlas 2018 (WOA18;  Boyer et al. , 2018). For each grid cell, we summed all biomass concentrations from the same sampling event, as different species were sometimes counted as separate measurements. Next, we averaged all biomass and abundance values per grid cell and month of the year over the top 200 m. This depth cutoff was deemed reasonable as 99. 1% and 99. 4% of the summed abundance of pteropods and foraminifers, respectively, stem from the top 200 m (Figure  S4  in Supporting Information S1). To better approximate a normal distribution, TC mass values were log-transformed with a log 10 (TC + 1) transformation for further analyses.
All modeling was conducted with the h2o 3. 36. 0. 3 R package (H2O. ai, 2021). For the GLM, we included both first and second-order dependencies on the predictors and assumed a normal distribution of the target variable with an identity link function  (Nelder & Wedderburn, 1972). In the GAM, we fitted smoothing terms for all predictor variables using cubic regression splines, the most common smoothing algorithm  (Hastie & Tibshirani, 1990) , and a normal distribution with the identity function as link for the target variable. For the RF, GBM, and DL, the hyperparameters were tuned using a grid search  (Boehmke & Greenwell, 2019d). Tables  S6, S7 , and S8 in Supporting Information S1 show the grid of parameters evaluated for each model. The final setup of the RF as determined from the tuning process (Table  S6  in Supporting Information S1) included 830 trees for pteropods and 330 for foraminifers.