dataesr/scientific-paragraphs-categorization huggingface dataset:
This was done from winter 2010 to early spring 2011. In this second sampling, all the symptomatic trees were sampled (thus, some trees were sampled several times for confirmation of previous molecular analyses; see below). To estimate the number of asymptomatic trees (data unknown at the beginning of the study but crucial to estimate the potential role of orchards as inoculum reservoirs), one out of every three trees was also sampled (i. e. , systematic sampling). Between 78 and 244 trees per plot were analysed, depending on the size of the plots (Supplementary Table  S1 ). A total of 2,656 samples collected in 69 different orchards were used in the study (Supplementary Table  S1 ). In parallel, we sampled wild blackthorn (Prunus spinosa L. ) or myrobalan bushes around the plots, at up to 30-40 km (Supplementary Figs.
Successfully amplified imp DNA was purified and Sanger-sequenced in both directions by Genewiz (Takeley, UK). Chromatograms were trimmed, assembled, and aligned using the Muscle algorithm, and visually checked under Geneious (version 5. 5) (http://www. geneious. com). Sequences were deposited in GenBank (accession n° MN116709 to MN116718; Supplementary Table  S6 ). SNPs between individual sequences were detected, and 'Ca. P. prunorum' genotypes were defined according to Danet et al. 35  When previously undescribed SNPs were detected, we performed a second independent extraction from the same sample, followed by amplification and sequencing to ascertain the new imp sequence. To represent genealogical relationships among sequences, we used POPART software  52  (v. 1. 7) to build a genotype network using the integer neighbour-joining (IntNJ) algorithm, which is well adapted for low-divergence datasets.
Statistical analysis. All statistical analyses were performed using R 3. 4. 0  53. A correspondence analysis was performed on the contingency table (Supplementary Table  S3 ) using the coa function in the ade4 package  54  , and we visualised the results with the factoextra package  55. The contingency table was also directly visualised using the table. value function in ade4 to uncover specific association patterns. To test whether the distribution of genotypes among compartments differed between the three regions, we carried out multinomial regressions with the nnet package, and we tested the interaction between compartments and regions by comparing (using a likelihood ratio test) the complete model (including the main effects and their interaction) with the model without the interaction.
Acknowledgements We are very grateful to  J. Peyre  for her valuable technical assistance, and the following individuals for their contributions to the collection of psyllid or plant samples:  N. Courtieu & J. -M. Broquaire  ( SICA Centrex ),  N. Galabert & J. Delnatte  ( SICA L'Edelweiss ),  B. Rouillé  ( SRPV-PACA ),  E. Navarro  ( Terroir de Crau ),  P. Delon  ( CA-Gard ),  E. Falezan  ( GIE-Tain l'Hermitage ),  P. Exbrayat  ( CA-Drôme ),  M. Léon-Chapoux  and  V. Delaunay  (SEFRA), as well as the 15 growers involved in this study. Part of this work benefitted from a postdoctoral grant to JP funded by an  INRA-CIRAD   SDIPS grant  ( Speciation and molecular Diagnosis of Insect Pest Species complexes ). Field and molecular work for this study were supported by the  Prima phacie project  funded by the  EFSA  grant agreement  CFP⁄EFSA⁄PLH⁄2009⁄01.
Still in the planning stage, Biogeoscapes 2 is one new global effort that proposes to do this across multiple temporal and spatial scales, with the ultimate goal of understanding how metabolic feedbacks and interactions underpin biogeochemistry and structure ocean ecosystems. A multi-dimensional global dataset of POC, analyzed using improved analytical methods and integrated with omics, physiological, and nutrient data, would be invaluable for addressing many of the still-unanswered questions about POC, including how to integrate POC composition into new modeling platforms or even machine learning approaches. In summary, several areas are promising frontiers for the study of POC: (1) Leveraging the power of structural diversity to identify sources and metabolic processes, (2) Effectively integrating omics and geochemical techniques, (3) Improving methods to characterize unidentified components of POC, (4) coupling isotopic (δ 13 C and 14 C) and molecular investigations of POC and DOC.
" Understanding coastal processes and how these ecosystems function therefore often requires an assessment of numerous interacting environmental variables covering all process relevant spatio-temporal time scales. The technology of cabled coastal underwater observatories has been significantly improved in recent decades  (National Research Council, 2003; Hart and Martinez, 2006; Witze, 2013; Favali et al. , 2015). Underwater observatories are often designed to provide ground truth data from static reference points over time  (Badeck et al. , 2004). In contrast to ship based surveys or other mobile observatory platforms such as AUV's and autonomous gliders, cabled underwater observatories, however, cannot provide spatial coverage of a certain area. Together with mobile systems such as Argo floats that are specifically designed to cover extended surface areas but with limited temporal resolution  (Levy et al.
However, a thorough discussion of precisely these failures, pitfalls and drawbacks is particularly important in the case of emerging technologies that are not merely a "flash in the pan, " but seem to be developing as new tools that enable major advances in science. New technologies must also provide a truly sustainable and long-term benefit for science. It is therefore necessary to consider the effort and the risks involved in operating cabled underwater observatories for science  (Buck et al. , 2019). In this manuscript, we describe the experiences gained from 7 years of operating of two cabled underwater observatories in the North Sea and Arctic. We present the basic design features of the node systems used, the data handling procedures as well as the design and procedural changes since the systems were commissioned.
A further capacity extension by upgrading the fiber optic transceivers is possible. The submarine cable is connected to the underwater node system at the "breakout box" (Figure  4 ). In this cable termination, the optical fiber connection of the underwater cable is converted into a copper-based data transmission. The incoming 1000 V are converted to 48 V to supply the electronic components in the breakout box. This large-step power conversion was achieved by a special power supply unit from SYKO Type BLG. M. The IT components used are active components with their own IP addresses to communicate with the components and check their function in the event that either no node is connected or an undefined error occurs in the system. The breakout box is constructed of polyethylene (PE) and is approximately weight-neutral in water.
An IP-based water intrusion detector is mounted to monitor it. Figure  5  shows the complete COSYNA Underwater Node System during operation. The breakout box is connected to the node by two wet-mateable cable connectors: one connecting FIGURE 3 | Sea cable (diameter 22 mm) used to connect the land station with the node system (1 = insulated cores, 6. 0 mm 2 ; 2 = filler; 3 = GRP fabric; 4 = fiberglass cable single mode; 5 = taping; 6 = outer sheath. For additional details see text). Photo: P. Fischer. the power (1000 V -type DC), and the other connecting Ethernet (1000BASE-T). Communication and data transfer from and to the underwater node systems is performed by standard internet protocol TCP/IP. From 2012 to 2015 at the Helgoland systems and until 2016 at the Svalbard system, the land stations of the nodes were connected via IP radio relays over a distance of up to 60 km to the respective national IP network.
Even though these connections were sometimes identified as possible bottlenecks for remote node operation, especially in the Arctic, they never restricted the required bandwidth. From 2015 onward at Helgoland (Germany) and from 2016 onward at Svalbard, a cable-based fiber optic connection via the respective national sea cable infrastructure is available for data transfer. The internal power of the underwater node system and connected sensors is set to 48 V to allow for safe underwater  houses a battery pack that provides power for 2-6 h, depending on the power consumption of the sensors. The right tube (2) is the node system which is connected to the breakout box by the red 1000 V power line and the blue Ethernet line. On the front panel, ten sensor ports are available, each providing up to 200 W and an Ethernet connection.
All cables between the different node components are wet-mateable by divers, except for the sea cable that enters the breakout box from below via a permanent cable feedthrough (see Figure  4 ). Photo: P. Fischer. operation by divers when the 1000 V land power supply must be shut down. To keep the system running during intentional (or unintentional) power cuts, an additional battery buffer is installed (Figure  5 (1)), keeping the system alive for at least 2 h so that divers can safely approach the system under fully operational conditions. For attaching sensors (or even complex sensor units) to the node, ten underwater mateable connectors are available per underwater node, each providing 100BASE-T ethernet link (max. 1000BASE-T ethernet) for data transfer, and a 48 VDC power supply with a maximum of 200 W per connector (Figure  6 , right image).
Standard Data Provided by the COSYNA Underwater Node System Each COSYNA Underwater Node System is connected to a standard lander (Figure  6 , left) carrying a sensor package that measures basic oceanographic parameters  (Baschek et al. , 2017)  continuously year round. It comprises an upward looking ADCP (Teledyne Workhorse 1200 kHz), sensors for pressure, temperature, conductivity, oxygen, chlorophyll-a fluorescence, and turbidity integrated in an extended CTD (Sea&Sun CTD90) and temperature logger (SBE38). All standard oceanographic parameters are publicly available in near realtime (based on the logger after 1 or 24 h) on the COSYNA data portal 8  and the AWI web page 9. Both data portals offer CSV formatted data for download. The COSYNA data portal offers additional SensorML format via the web service OGC-SOS, the AWI dataportal offers JSON format.
Discussions in the scientific community revealed, that most biological oceanographers prefer the CSV format, more standard oriented scientists prefer SensorML or netCDF and data scientists often prefer JSON. Even though the three latter data formats are more efficient with respect to information per data volume, according to our experience it is highly recommended to at least provide one "low-level" data format for download to make data accessible in the context of FAIR also for nondata specialists. On the other hand, CSV formatted data do not fulfill the FAIR criterium of interoperability because CSV files are not per se machine readable and linkable. It will certainly require further efforts to implement the FAIR standards for all user groups. An important step in this direction would be the consistent implementation of simple to use import routines for FAIR data formats in the most common spreadsheet programs and the provision of easy-to-use import routines for FAIR data formats in the common script languages such as R or Matlab.
There, the user must demonstrate that the sensor will function properly on a computer for at least 24 h with the defined power supply and that the software used for data acquisition (e. g. , the original software from the sensor manufacturer) will demonstrate working stability. The final implementation of the sensors in the node system, including the mechanical, electrical and IT integration of the sensor as well as setting up and managing the user access to the sensor, is managed by the COSYNA node consortium, in which the two participating partners -Helmholtz-Zentrum Geesthacht and Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research -are represented. New sensor integration into the Arctic underwater node system is more extensive than for the North Sea node, as there, sensors are only accessible once or twice a year.
Access to the virtual machine is provided through remote login via an open source or a commercial remote login program, whereby the programs "Real-VNC" 10  and "TeamViewer" 11  have become the most popular in the COSYNA consortium. The user has full user rights to install software on his or her workstation to operate a sensor, and each workstation has a standard hard disk size of 500 GB to temporarily store sensor data. This system architecture allows the user to operate a sensor from anywhere in an identical manner and with the same software as used directly in the laboratory without the underwater node system infrastructure. Data acquisition is important with respect to the software required for continuous sensor operation. For many sensors, only interactive sensor control software is available that requires manual interaction to store data files, read calibration data, or perform other operations.
The development of software that allows fully automated operation of sensors, including data storage, is usually costly and requires special programming for each sensor type. Within the framework of the node system development, we developed an alternative way to operate sensors permanently and resiliently without an additional probe-specific software solution. For this purpose, the software "MacroScheduler" was used to code every action a user performs on the screen with a keyboard or mouse into a stable executable program. With this procedure, it has been possible to fully automate any original sensor software thus far. This has the additional advantage that the generated data files can be read with the original software and, if necessary, processed further. In parallel to the optimization and development of the node hardware, the importance of timely, resilient and, in particular, traceable plausibility and quality checks of the measured data emerged in the course of the operational phase.
Especially in case of cabled observatories, the tendency and the willingness is great to feed the measured data directly into respective databases and thus to make them immediately available to science and to public stakeholders, especially when the financial support of the systems may depend on this "real-time" data availability. Without reliable and widely automated quality control procedures, there is a considerable risk that unreliable or, in the worst case, false data, e. g. , due to sensor failures or sensor drift, may become available and be used by the scientific community or the public. Furthermore, initiatives such as "FAIR"  (Wilkinson et al. , 2016)  address the importance of adequate metadata for each sensor without which it is often not possible to use the data for scientific analyses.
These sensors provide large datasets of high-resolution images of a certain water volume or benthic area. The images are transferred online directly to Germany, where they are analyzed for total species abundance, species composition, and species-specific length-frequency distributions  (Fischer et al. , 2007; Wehkamp and Fischer, 2014). Even though optical systems can be deployed also autonomously, cable connected systems have the advantage of more or less unlimited power supply and storage volume for the images. Furthermore, image analysis is often time consuming especially when no fully automated object detection and measurement algorithms are available, a field of data science in aquatic ecology which is just emerging  (Marini et al. , 2018). Images are delivered in near-realtime every day and can be analyzed continuously which is often more feasible than analyzing thousands of images after an instrument has been recovered.
The water column in the littoral zone is thus completely assessed once a week, with 2 days to spare for repeating depth strata that were missed -for example, due to technical problems or poor visibility. The system facilitates measurements of species abundance, species composition and species-specific length frequencies, while providing unique time series over the 24 h diel cycle continuously for 365 days of the year. The observatory enables repeated sampling every year as shown in Figure  7  (right panel) for the season 2017-2018. This long-term sampling provided the world's first year-round dataset of the littoral fish community in an Arctic fjord system and confirmed the hypothesis that the polar night is rather important for the fish and macroinvertebrate community in very shallow areas.
The Node Hardware Experience has shown that almost all generic and sensor-specific developments or experimental designs required significantly more time in operation than industrially tested software and hardware. Nevertheless, for some experimental approaches, no off-the-shelf components are available, so that in-house developments are necessary. However, this decision should be carefully examined on a case-by-case basis, as industrial solutions sometimes do exist, which are, however, more expensive initially. For off-the-shelf solutions, however, the financial expenditure is shifted from the investment to the operating expenses. It is important to consider that repairs or adjustments during operation are always associated with the risk of data failure or loss. In addition to several small changes and optimizations that have occurred over the years during the operation of the nodes, three major problem areas have emerged, each of which has had a lasting effect on the operation of the underwater node during certain phases.
Node Control Software The overall power management of the underwater node (switching the individual ports on and off and providing power to the sensor ports) as well as the node monitoring (power consumption and network activity) is realized by Programmable Logic Controllers (PLC) with discrete software. The first prototype node used Siemens Simatic S7 PLC, which was replaced in rebuild by a Beckhoff CX8090 CPU. Both PLC solution were equipped with required analog and digital I/O modules. The remote control and monitoring is realized via a web frontend and IP, and all available information are logged in a SQL database for system monitoring. This frontend has three access levels: "user, " "port administrator" and "system administrator. " As of now, "users" are allowed to see the status of all ports (i.
Software Issues With Respect to Sensor Operation In the very beginning of node operation, two different scenarios of sensor operation were planned: (a) the operation of sensors for standard parameters by the node consortium itself and (b) the operation of sensors from external partners under the full responsibility of the external users. The external users, in particular, were thought to be fully responsible for their data and, after the initial installation phase, also for the remote sensor operation and monitoring. Both scenarios were adapted based on the experiences of the first year of node operation. Scenario A was initially designed as a type of real-time operation, where the sensor data were to be streamed directly to a central database at the Helmholtz-Zentrum Geesthacht. While the basic principle of this real-time streaming approach works well for our set-up and is still in place, some shortcomings of a pure streaming procedure became apparent.
Many sensors do not deliver "to go" data directly from the sensor itself but "raw data, " such as voltage, a digital or a binary output. This data must be processed by software using calibration coefficients or conversion algorithms to obtain the target parameters in the correct units. With direct streaming, the raw data (e. g. , Volt) are converted by generic software "on the fly" into scientific values which are then directly fed into the database, however without storing the original raw (e. g. , Volt) data. This holds a considerable risk in case the calibration files are technically decoupled from the probe and can thus be unintentionally confounded. In 2014, this "on the fly" conversion resulted in almost 2 month data loss from a specific sensor, because the wrong calibration file was used.
Because the raw data (Volt) were not stored, the scientific data could not be recalculated with the correct calibration file. To prevent this, it was decided not only to stream the final scientific readings from each probe, but also to store the raw data from each sensor in the highest possible temporal resolution (e. g. , in volts at 1 Hz) every hour in single files. This makes it possible, in case of accidental use of the wrong calibration file, to recalculate the data completely afterward. Additionally, it was decided to implement additional security procedures for the data transfer to the respective databases to avoid the transmission of erroneous data in the data portals and to ensure that there is no missing metadata for individual sensors. From 2016 on, the transfer of data into the database itself was obligatorily linked to the availability of a minimum of up-to-date metadata which means that if metadata were missing, no data entry would be possible at all.
This method proved to be unsuccessful, with a high risk of data loss for external sensors. Most external users are able to handle individual data files from their sensors, but are overwhelmed when the same data files must be continuously processed. Data files are then often stored unsystematically, locally and without the necessary backups. Based on this experience, the "data policy" for handling external data was changed in such a way that all data, if the sensor owner agrees, are also stored in the corresponding COSYNA databases and are available there in the highest available resolution via a password-protected web interface  (Breitbach et al. , 2016). A last major lesson learnt in the course of long-term automated sensor operation at our underwater node systems addresses data management and data verification procedures  (Vallejos and Morimoto, 2013).
ACKNOWLEDGMENTS We express our ardent thanks to  Max Schwanitz  from  AWI  for his key contribution during the construction phase and the continuous operation of the AWIPEV observatory from 2011 on. We furthermore want to express our thanks to  Uwe Posner  ( 4H-JENA engineering GmbH, Mühlenstraße 126, 07745 Jena, Germany ) and  Reiner Loth  (loth-engineering GmbH, Lochmühle 1, 65527  Niedernhausen, Germany)  who both were deeply involved in the development and operation of the FerryBox and underwater node systems at  Spitzbergen and Helgoland. We are grateful to the  AWIPEV  staff  Rene Buergi ,  Verena Mohaupt ,  Marine Ilg ,  Rudolf Denkmann ,  Basile Le Maut ,  Grégory Tran , and  Gwendal Hénaff  who made the continuous operation of the underwater observatory in this remote location possible.
The need for incremental methods for the training phase of machine learning models has revived the interest in the stochastic gradient descent (SGD) method  [30]. SGD sets g k = ∇f i (x k ), where i is an index chosen from [n] def = {1, 2,. , n} uniformly at random. SGD therefore requires only a single data sample to complete a step and make progress towards the solution. Thus SGD scales well in the number of data samples, which is important in several machine learning applications since there many be a large number of data samples. On the downside, the variance of the stochastic estimates of the gradient produced by SGD does not vanish during the iterative process, which suggests that a decreasing stepsize regime needs to be put into place if SGD is to converge. Furthermore, for SGD to work efficiently, this decreasing stepsize regime needs to be tuned for each application area, which is costly.