dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). Brodu et al. (2017)  describe a super-resolution method which is fully compatible with atmospherically corrected Sentinel-2 imagery. Their approach uses geometric features which are not band-dependent in order to meaningfully re-sample the 20m and 60m bands in Sentinel-2 data to 10m. This method is available as the Sen2Res plugin for the ESA SNAP open-source software designed for the processing and analysis of ESA products. The plugin delivers all 12 image bands stacked in a single tif file. However, in this work it was decided not to use bands 1 and 10 which were designed to detect atmospheric quantities and work a maximum of 10 bands. Pixels in the tif file have values normalized from 0 to 1 which is convenient for the modelling steps that will follow. At this stage, the only obvious drawback of this super-resolution approach is computational cost, it took approximately sixhours to produce super-resolved bands cropped to our 16 survey areas.
Data preparation We begin this step by assessing the co-registration between the satellite and drone data. This task is not straightforward. Normally, positional accuracy of satellite is assessed with relatively large stable, often man-made, features. No such features are available in the drone imagery. We therefore limit ourselves to a qualitative check that the landform contours in both drone and Satellite imagery overlap in the same Sentinel-2 pixel. However we note that even this determination is not simple as partial occupancy pixels can raise questions as to the exact location of a boundary in the Sentinel-2 data. However, a validation of the UAV-derived orthoimage with additional data from the RTK-GPS gives an average positional accuracy for the drone orthoimagery of 0. 09m. Co-registration was therefore deemed adequate.
In order to compare the classification performance of models trained with UAV-derived models to those trained without field data, we first generate a set of so-called 'desk-based' polygons. In this case we use photo-interpretation of the Sentinel-2 images themselves in order to delineate areas falling within our three classes. This is simply done in QGIS 3. 4 by displaying the Sentinel-2 image in false-colour with an infrared component and using a polygon shapefile to manually digitize class samples. These vector class samples are then rasterized to outputs where each pixel holds the class value. Areas that were not digitized are coded as 0 and will eventually be ignored. We will refer to this data as the desk-based label raster. After this step, we organize the data by cropping the UAV-derived labels, the Sentinel-2 imagery and the desk-based class rasters to small areas around the study sites.
Therefore for each of the 16 UAV acquisitions, we have three small georeferenced rasters: a Sentinel-2 sub-image (10 bands), a UAV-derived label raster (1 band) and a desk-based label raster (1 band). Fuzzy membership class values for each Sentinel-2 pixel can now be determined by extracting the map coordinates of the Upper Left (UL) and Lower Right (LR) corners of each Sentinel-2 pixel. Within this bounding box, we extract the associated 10000 classified pixels from the 10cm ×10cm UAV-derived labels and then calculate membership percentage for each class in each Sentinel-2 pixel. Finally, the data is prepared for machine learning. For readers less familiar with machine learning, we recommend Burkov (2019) as a concise text leading to more advanced texts (e. g. Goodfellow et al. , 2016; Chollet, 2017).
In the case of CNN data, the fourth dimension is merely an indexed list of images and there is no implication of time. The tensor format is therefore a convenient way of stacking multiple images in a single digital file. In the case of large images, CNN can be used to model local information such as semantic class (e. g. Buscombe and Ritchie, 2018). However, such image-specific CNN models usually require the use of a small tile which gives a local sub-sample of the image. In our case of sub-pixel fuzzy classification, we will initially assume that the exact size of this local region is small and will determine the optimal size later. We therefore use Python to create and format our data as 4D tensors of small image tiles. We scan each Sentinel-2 image and for each pixel extract an (S, S, 10) sub-image tile where S is the size of the tile, in pixels, and 10 is the number of bands.
CNN model selection We now select a CNN model for use in this work from a range of possible designs. We use the Python coding language and the Tensorflow library  (Abadi et al. , 2015). The tile size mentioned earlier must be selected. In addition, there is a very long list of tunable parameters to be adjusted and architecture choices to be made. We tested a total of 102 candidate models that spanned a range of values for the tile size, the depth of the neural networks, the number of convolution filters (discussed later) and the optimal input bands to use from the possible 10 Sentinel-2 bands intended to sample surface characteristics. For brevity we only present summary findings here. The full analysis is available in the Supporting Information document accompanying this article. Our model selection procedure arrives at three conclusions.
Experimental structure We structure this work around three experiments. These use our available data in different partitions of training and validation samples that will allow us to test and demonstrate key aspects of model performance. The main condition in all these experiments is that data used to train models will never be used to validate these models. There will always be a clear separation of time and/or space between training and validation datasets. Readers are also reminded that our initial 52543 samples have undergone a data augmentation procedure and that we now have a total of 210172 samples at our disposal for training UAV-based models and 79650 samples to train desk-based models. However, for both UAV-and desk-based models in validation, we remove the augmented samples and only use raw data.
Our first experiment aims to test temporal resilience of trained models. We use label data acquired in 2018 to train models that will be validated against data acquired in 2017. Our second experiment aims to test the multi-river spatial transferability of models. We use label data acquired for the Rivers Sesia (2018) and Bonamico (2017 and 2018) to train models that will be validated with data from the Rivers Po (2017 and 2018) and Paglia (2017 and 2018) and imaged on separate Sentinel-2 tiles. The third experiment tests local spatial transferability of models. We wish to assess if local label data acquired from one or two sites could be used to classify whole, but single, rivers. This experiment is therefore separated in three parts (Rivers Sesia, Paglia and Bonamico) and we use label data from one or two sites to train models validated against an additional separate site.
For the fuzzy classification tests, we use two comparator methods: linear unmixing and the DNN from Figure  6. We implement linear unmixing with the Orfeo Toolbox used as a plugin for QGIS 3. 4  (Grizonnet et al. , 2017). This workflow starts by determining endmembers with the automated method of  Nascimento and Dias (2005). Here we input three classes. Then, the abundance fraction of each component (the fuzzy classification) is estimated with an unmixing procedure using the Minimum Dispersion Constrained Nonnegative Matrix Factorization algorithm  (Huck et al. , 2010). This is a fully unsupervised procedure and it runs on individual images. It was therefore executed on each Sentinel-2 cropped image of our original dataset and we produced a total of 16 outputs, one output for each line of Table  1.
Finally, we show how fuzzy class rasters produced with our cCNN model can be deployed at larger scales. We trained a master model using all 210172 training samples. We then applied it to produce a total of 294 linear kilometres of river corridor classifications for the Po, Sesia, Paglia and Bonamico Rivers as imaged in 2018 by Sentinel-2. These cannot be clearly displayed in static format, but they have been made available to the reader in the online data and can be opened by any GIS package. In order to demonstrate a large-scale application, we use a 10km stretch of the River Paglia and show how our approach can detect meaningful net change over a oneyear period. Figure  14  (along the diagonal) shows this 10km reach of the Paglia for years 2017 and 2018. Figure  14  also shows insets for three sediment bars, A, B and C, where vegetation growth can be seen.
Discussion We have demonstrated that classification models, both crisp and fuzzy, trained with the benefit of ground-truth data derived from low-altitude UAV flights systematically deliver better performance. Our cCNN, trained with UAV-derived label data, delivers optimal performance for both crisp and fuzzy classification of Sentinel-2 data and it can identify pure class pixels with an accuracy up to 99. 9%. Similar models trained with desk-based data, not having the benefit of field observations, identify pure class pixels with a reduced accuracy of 81%. In terms of fuzzy classifications, we find that our cCNN delivers the optimal error statistics (Table  4 ) confirmed by well structured error distributions (Figures  9  and 10 ) and visually accurate fuzzy classification outputs. Conversely, any modelling approach that did not benefit from the UAV-derived labels had markedly degraded performances (Table  3 ), poorly structured error distributions (Figures  7  and 8 ) and unsatisfactory fuzzy classification outputs (Figure  11 ).
2% and mean absolute error of 14. 2%). This demonstrates that fuzzy models can transfer to different Sentinel-2 tiles of the same location, but for different times. This is an interesting finding for regional studies aiming to monitor/characterize several rivers over a period of several years. As a consequence, it is not necessary to fund annual field survey, but it is sufficient to guarantee a biennial UAV acquisition to achieve a good model performance. In our second experiment we show that UAV surveys acquired at for two rivers can produce models that can satisfactorily predict fuzzy memberships for two new rivers as imaged in two separate Sentinel-2 tiles (median error of 5. 5% and mean absolute error of 18. 0%). The obvious caveat here is that all our field sites are in Italy. Whilst Italy does have a very wide range of river types with strong north-south gradients of temperature, hydrology and geology, our data obviously does not include tropical rivers, arctic rivers, bedrock rivers, etc.
Overall, the errors reported here appear slightly higher than reported by  Foody et al. (1997). These authors use Landsat 7 data at 20 m in a similar three-class problem in order to develop a fully fuzzy model for AVHRR data with a resolution of 1. 1km. We note that for the testing data, these authors find a best RMS error of roughly 8% to 9%. We argue that this difference in quality can be explained by the resolution of the label data. When using Landsat data at 30m spatial resolution, the scale of the Landsat pixel remains much larger than the small features such as individual trees and channels. The Landsat acquisition at 30m will have the usual smoothing effect on the edges of natural features smaller than 30m. In our case, the use of hyperspatial UAV-derived label data at 10cm implies that our training data approaches the full spatial variability found in our study landscape.
Acknowledgements-This work was performed in the framework of the ' IRIS -Italian Research and development Initiative for Spaceborne river monitoring ' project and funded by the  Italian National Institute for Environmental Protection and Research (ISPRA)  in the form of a technical-scientific collaboration agreement between  ISPRA  and the  Polytechnic University of Milan ,  Department of Electronics, Information and Bioengineering , within the context of the  ASI-ISPRA initiative 'Habitat Mapping '. Impact activity on this project has been supported by  Department of Geography Impact Fund, Durham University. The authors are grateful to the  EU Copernicus Programme  for freely providing Sentinel-2 data that was exploited in this work. Our thanks go also to the regional Institutions of Piedmont, Lombardy and Calabria Regions that made available discharge and precipitation records used to control hydraulic and hydrologic conditions of the monitored sites.
The exposure time did not impact the robustness of the NO 2 results. Two sets of portable online particle instrumentation were deployed inside the stadium and in the warm-up area. These units monitored the fine-scale temporal variation of regulatory (PM 2. 5 ) and novel parameters (ultrafine particles, UFP; black carbon, BC), with known links to adverse health effects. UFP number concentration (N) and mean diameter were monitored with DiSCmini monitors (Testo), with 1-min time resolution, detecting particles up to 700 nm (accuracy: ± 500 # cm -3 )  (Fierz, 2011). The instrument's concentration range is from about 1000 to over 1,000,000 # cm -3. BC measurements were carried out with MicroAeth AE51, with 5-min time resolution, providing data on Equivalent Black Carbon (BC, in μgm -3 ) derived from absorption values.
Wind speed and direction were monitored both in the stadium and in the warm-up track by means of a LCJ capteurs anemometer. Finally, sensor nodes (Kunak AIR) were also deployed at each of the monitoring sites. These monitors incorporated electrochemical sensors for phase gas pollutants NO 2 , O 3 , NO and CO, a laser-scattering sensor for particulate matter PM 1 , PM 2. 5 , PM 10 , and temperature (T), relative humidity (RH) and pressure (P) sensors. Data was provided with a 5min time resolution. Sensors were calibrated by co-location at a reference air quality monitoring station in Barcelona during 1 month prior to shipment to Yokohama, and by deploying them side by side during one day inside the Yokohama stadium, prior to the training period (May 8th, 2019). The results from the co-location exercises are shown in Figs.
Questionnaire design and data collection process A specifically designed questionnaire was self-administered by athletes, using tablets. The goal of the questionnaire was two-fold: 1) to assess the degree of concern of athletes regarding air pollution, and 2) to evaluate the suitability of the questionnaire design for epidemiological assessments (missing, redundant information, etc. ) in future competitions. The questionnaire collected information on basic characteristics, including place of residence and training, medical background information, performance information and exacerbation of the previously reported diseases, and a section on air quality perception. The list of diseases in the medical section was not exhaustive and requested information only about pulmonary, allergic or cardiovascular chronic disease, specifically asthma, allergy/atopic reactions, frequent acute lower respiratory infections, high blood pressure and others.
The multi-linear model showed a statistical link with wind speed (p-value b 0. 01), which was weaker than in the case of PM 2. 5 and BC, with a multiple R-squared of 0. 18. The model could not be applied to NO 2 , CO and O 3 from the sensors given that temperature and humidity are included as input in the sensors' calibration algorithm. To further analyze the hourly variation of the air quality parameters, with different origins and physicochemical properties, a k-Means clustering was applied to the dataset, using an optimal 3 cluster solution. The k-Means algorithm separates the data into k clusters where k is an integer given by the user. The analysis was carried out on the scaled hourly averaged daily profiles. Fig. 5  summarizes the main information of the time variation of each cluster.
-The availability of high time-resolved exposure data in the stadiums opens up the possibility to calculate doses of specific pollutants  (Larkin and Hystad, 2017; Ramos et al. , 2016)  for individual athletes, which would be a highly valuable information for athletes and trainers. Inhaled doses could be calculated for the time spent by athletes in the training and competition venues, but would however be unavailable for the time spent outside these areas. -In future studies, the combination of air quality and health data (not collected in this study) may enable us to determine the prevalence of pollution-related diseases (respiratory, atopic and cardiovascular) in athletes. -Also in future studies, combining real-time air quality data and reports of exacerbations of diseases, will enable the association between the prevalence of respiratory and atopic diseases and exacerbations of symptoms, and competitions results and specific pollutant levels.
Acknowledgements This work was partly funded by  World Athletics ,  AGAUR  (project  2017 SGR41 ) and by  H2020 SME (K-Smart Environment Project ) Grant agreement ID:  823487. The significant contributions of  Mrs. Mariko Yamada , senior manager at the  Japan Association of Athletics Federations ,  Mrs. Yuchen Sun  (volunteer),  Mrs. Mahgol Zahra Kamari  (volunteer), and  Mrs. Xiao Jun  (volunteer) are acknowledged here.
Because these cultures resided in personal collections, the goal was to create a repository with infrastructure that would persist and facilitate continued research on the group. Support from the U. S. National Science Foundation's program Collections in Support of Biological Research made the transition possible. The collection is now housed at the University of Michigan's Research Museums Complex (RMC) in a modern cryopreservation facility with liquid nitrogen freezers (Fig. 1 ). Here we describe the contents of the collection from a taxonomic and phylogenetic perspective. Combining existing DNA sequence data with sequencing data we acquired from CZEUM with Oxford Nanopore Technologies (ONT) single-molecule sequencing, we developed a comprehensive phylogeny of a large percentage of isolates in the collection.
2006 (Letcher et al. , 2008;; Mozley-Standridge et al. 2009; Simmons et al. 2009; Longcore and Simmons 2012b; Karpov et al. 2014 Karpov et al. , 2016;; Seto et al. 2020). Additional orders are likely to exist as evidenced by environmental DNA studies that show a poor match between cultured diversity and uncultured  (Freeman et al. 2009; Davis et al. 2016). The proper identification and cataloging of culture collections will not only add to our knowledge of basal fungal diversity, but also will provide DNA information to public databases, information that is necessary to identify and characterize novel diversity detected with environmental DNA sampling techniques. The most biodiverse collection of zoosporic eufungi was the University of Maine Culture Collection (UMCC, or JEL collection) begun by Joyce E.
We have begun to characterize the diversity of the CZEUM with ONT sequencing technology and our results compare favorably with a sample of Sanger sequencing products. Herein we describe the current holdings of the CZEUM and the efforts made thus far to determine viability and phylogenetic diversity of the cultures therein. We hope to make this fungal collection a leading resource for the study of zoosporic eufungi by providing researchers with vouchered cultures and accepting new cultures for permanent cryopreservation. We have amassed previously-generated GenBank accessions and new ONT sequences from 77% of the publicly available CZEUM cultures (excluding Bd), with 61% possessing sequence data for all three (18S, ITS, 28S) rDNA operon regions, and we provide a comparison of ONT sequencing technology versus Sanger sequencing products.
Cryopreservation facility, collection transports, and database construction The CZEUM is maintained in a cryopreserved state at the University of Michigan RMC cryopreservation unit. The cultures are stored in up to five replicate 1. 2 or 2 mL screw-cap cryovials in 81-cell storage boxes in a Custom BioGenic Systems (CBS) Isothermal V-3000-AB Series cryopreservation freezer (hereafter RMC freezer) that is liquid nitrogen cooled (Fig. 1g ). At this time, all cultures are maintained in a single freezer; future goals include dividing replicates to other storage units or facilities. No liquid makes contact with the preserved specimens, making their handling relatively easy and safe with minimal personal protective equipment, i. e. , cryogloves and face mask. The RMC freezer is on a CBS 2301 Series automated filling system, with daily scheduled fillings, and automated fillings if liquid nitrogen levels reach a set minimum.
We transported the UACCC from the University of Alabama to the RMC overnight on dry ice in a single large cooler containing all cultures and placed cultures in the RMC freezer immediately upon arrival to the RMC. We received Allomyces isolates from the FGSC in individual 1. 5 mL microcentrifuge tubes containing strips of filter paper, onto which resting spores had been dried. We transcribed digital and hard-copy notes from the UMCC, UACCC, FGSC, and University of Michigan culture collection into a database in Microsoft Access, which is regularly updated and uploaded to the CZEUM online searchable database (czeum. herb. lsa. umich. edu). The CZEUM collection is integrated into the fungarium of the University of Michigan Herbarium, leveraging shared infrastructure and collection management experience.
2018), converted fastq to fasta for downstream processing with Seqtk (https://github. com/lh3/seqtk), and demultiplexed the single experiment fasta file with Minibar (Krehenwinkel et al. 2018). We attempted assemblies of demultiplexed samples in Canu 1. 9  (Koren et al. 2017) , accepting the output of the first setting that yielded an assembly composed of ≥20 reads, with four settings from the manual and developers' recommendations  (Koren, pers. comm. ) , listed here by highest to lowest priority: (1) "faster" default setting (overlapper = mhap utgReAlign = true), (2) smash haplotypes setting (corOutCoverage = 200 correctedErrorRate = 0. 15), (3) continuity improvement setting (corMhapSensitivity = normal), and (4) metagenomics setting (corOutCoverage = 10,000 corMhapSensitivity = high corMinCoverage = 0).
Molecular phylogeny For our phylogeny of the Chytridiomyceta and Blastocladiomycota, we focused on phyla held in the CZEUM, i. e. Chytridiomycota, Monoblepharidomycota, and Blastocladiomycota, and thus excluded Neocallimastigomycota from the analysis. From GenBank, we selected 28S rDNA sequence data from cultured zoosporic eufungi, and added 18S rDNA sequence data when available from the same culture. This led to rDNA sequence data for 451 cultures (Table  S4 ), to which we aligned ONT rDNA sequences generated from 431 CZEUM cultures (Table  S5 ). We concatenated GenBank sequences in Geneious 9. 1. 8, aligned the complete data matrix of 882 sequences in  MAFFT (v. 7. 310) , and edited ambiguously aligned regions in TrimAl (v. 1. 2). We conducted ML best tree searches and calculated bootstrap percentages from 500 replicates in  RAxML (v.
CZEUM database and collection biodiversity As of Feb 2020, the CZEUM contains 1045 publicly available cryopreserved cultures, spanning the Blastocladiomycota and Chytridiomyceta (Table  1 ). The collection includes 52 species ex-type and lectotype strains and 752 of the cultures have proven viable. The CZEUM can be accessed at czeum. herb. lsa. umich. edu, where it can be searched on multiple fields (e. g. , genus, substrate) and strain requests can be made. Strains are distributed for a recharge fee under a fixed, standard material transfer agreement.