dataesr/scientific-paragraphs-categorization huggingface dataset:
Slurry dry matter may not sufficiently characterise the potential for infiltration, and information on bedding and viscosity may be useful (but possibly still insufficient). Useful details on emission measurement methods include anything that could vary among experiments. The location of weather measurements should be reported. In ALFAM2, weather data are reported as averages over each measurement interval. However, since variation in weather within an emission measurement interval may affect average emission, a higher resolution (e. g. , 10 min averages) may be more useful for model testing and development (and should typically be available). Standardisation of measurement methods would also be valuable. Slurry pH provides a particularly important example. Additionally, it is important for both researchers making measurements and those who use the resulting values to be clear about the purpose of the measurements.
Conclusions The ALFAM2 database is a large and diverse collection of NH 3 emission measurements from manure, and it should be useful for research on, and management of, NH 3 emission. High variability in NH 3 emission within the database is a product of a broad range of application, slurry, weather, and location conditions, as well as differences in measurement techniques, and other, unknown factors. Emission from cattle slurry applied to grassland shows clear effects of application methods, and effects of slurry characteristics and weather, which suggests that the data could be useful for model development or testing. Comparing NH 3 emission among locations will be difficult without assumptions about measurement accuracy and the source of unexplained effects. The database can be used in a myriad of ways, although users should be aware of dependencies among observations, confounding among variables, and imbalance when working with these data.
Other notes: Data from most ALFAM2 institutes have been presented in publications (publication codes used in database are included in square brackets after each citation, if available): 106 (NMI): Huijsmans et al. , (2001) , 201 (AAFC): Chantigny et al. , (2004)  (3), Chantigny et al. , (2007)  (2), Chantigny et al. , (2009)  (1); 203 (ARDC): Bittman et al. (2005)  (2), Bhandral et al. , (2009)  (1); 204 (AT): Misselbrook and Hansen (2001)  (3), Hansen et al. , (2003)  (2), Hansen et al. , (2004)  (4), Hansen and Birkmose (2005)  (1); 205 (AU): Thomsen et al. , (2010)  (1), Nyord et al. , (2012)  (2), Nyord et al. , (2013)  (4); 206 (CAU-LU): Gericke et al. , (2012)  (2), Ni et al. , (2012)  (1), Ni et al. , (2013)  (3), Koester et al. , (2014)  (4); 207 (INHeHAFL): Häni et al. , (2016) (1); 208 (INRA): Génermont et al.
Acknowledgements Funding was provided by provided by  Grønt udviklingsog demonstration program  ( Gylle-IT), Ministeriet for Fødevarer, Landbrug og Fiskeri-NaturErhvervstyrelsen. We thank  Normand Bertrand  ( AAFC, Quebec ) for organizing AAFC datasets,  Philippe Rochette  ( AAFC, Quebec ) for contributing data, and  Philipp Trénel  ( Teknologisk Institut, Aarhus ) for advice on mixed-effects models.
However, currently, very few attempts have been made to use Human beings as observation platforms. In this paper we describe large datasets (more than 200,000 pictures) that have been recently collected along the coast of Mauritius by using popular and cheap platforms such as kite surf and Stand Up Paddle. We describe the characteristics of the data collected and showcase how they can be geolocated and used to complement remote sensing and mapping in order to drastically extend the current scope of "old school" fieldwork. We point out some of the main limitations encountered which need to be addressed to foster this citizen science approach such as data storage and transmission, deep learning to automate image recognition. The methods are all based on open source softwares.
This is only starting with big vessels, AIS and initiatives like Global Fishing Watch  (Kroodsma et al. , 2018). Everybody can not afford a sailboat like Tara but many already have both action camera and boards (surfboards, kitesurf, SUP. ) which could be easily equipped to become efficient platforms for coastal surveys. Action camera are not used for every single session since conditions do not worth it all the time. However, instead of collecting images or videos about the unbelievable performance of the riders, action cameras, if turned underwater, might instead help to collect data about aquatic ecosystems. By doing so, we believe that the data deluge can become real for multiple coastal ecosystems and bring a lot of benefits and services to the society. Long term survey of coral reefs in overcrowded spots can quickly become a reality.
Open Source Softwares This project aims first at providing the hardware and softwares for anybody interested in collecting data while surfing. To achieve this, a set of open source codes has been created to deal with the collected data. All codes (mainly R) can be executed on a Linux OS: • As delivered by the GPS sensor, data are provided with proprietrary tcx format which can be turned into GPX data by using the cycleRtools package or online applications (eg gpsvisualizer. com). GPX format can then be then turned into any spatial data format using gdal library. • gpscorrelate has been used to geolocate underwater pictures colleced by action cameras. • metadata of deployments (sessions) have been edited with a simple CSV which can be uploaded and collaborativaly edited as a google spreadsheet.
The google spreadsheet is then programmaticaly accessed from R (by using the gsheet package) and turned into OGC 19115 metadata standards for geographic information (by using the R geometa package  (Blondel, 2017)  to generate ISO 19115 metadata and geonapi to push metadata directly in a Geonetwork catalog). Such codes can be compiled online by using RStudio server. • pictures metadata have been extracted by using the exifR package and are then stored in a PostgreSQL / Postgis database to improve data management (including tags of images to train Neural Networks). • maps have been generated by using QGIS (QGIS Development Team, 2009). All codes used to synchronize GPS and camera, rotate images and create metadata will be made available online in the coming months.
Methods / deployments First tests have been performed on coastal areas with clear water in order to collect underwater pictures. Our primary goal was to test our method to collect data on coral reefs habitats. Data presented in this paper all come from lagoons and related external slopes of Mauritius. However, it is clear that similar data can be collected wherever water is clear and shallow enough and where people can practice similar activities (snorkeling, paddle, kayak. on rivers, fjords, abers,. ). Data described in this paper, have been mainly collected by using a kite surf and a paddle but we tested the method by snorkeling and surfing. As explained in 2. 2, kite surfing is particularly interesting to cover wide areas with shallow water and it enables to combine both aerial and underwater surveys.
Underwater pictures Depending on the platforms (kite, paddle. ) the speed and thus image quality and covered areas are very different. The Figure  5  gives examples of underwater images (with a very good quality). As human beings we tend to only consider good quality pictures: good light and visibility as well as other aesthetic criteria. However most of data collected when surfing are collected randomly since the rider don't know exactly when the picture will be taken. Indeed, when riding at 20 knots, images are taken every 5 meters if the snapshot mode is parameterized with a half a second frequency. For what regards blurry images, it is important to remind that current image recognition algorithms can deal with noise and low quality data to recognize habitats. Convolutional Neural Networks have shown their ability in making use of low quality images.
Repositories for open data storage and access Making these data open by uploading them on Zenodo (except potential issues with endangered sedentary species) has been identified as a good way to ensure long term data archiving, dissemination and citation (DOIs). Various repositories can be used for such research data: • Zenodo and pangea to store raw data • Kaggle will be used as another channel to disseminate resized pictures (since deep learning requires lighter images). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume  XLII-4/W8, 2018 FOSS4G 2018 -Academic Track, 29-31  August 2018, Dar es Salaam, Tanzania However, since sharing location of certain species can become an issue for biodiversity (e. g. fostering illegal fishing), we plan to set up an image recognition algorithms prior to any data sharing.
Metadata and data access Data discovery is driven by metadata. In addition to pictures metadata (exif), we decided to describe the series of images (meaning the sessions) in order to enable spatial and temporal queries. OGC metadata have been generated by using google spreadsheets and open source codes (for both R packages and scripts). R geometa package  (Blondel, 2017)  has been used to generate ISO 19115 metadata and geonapi to them in Geonetwork. Metadata from different deployments can be stored in a Postgres / Postgis database. Usually the raw data record is a picture which might need to be resized or rotated (eg for image recognition through neural networks) and has to be geolocated. Once underwater pictures collected, they have thus been processed to correlate the clock of onboard action cameras with GPS data clock (by correlating the watch of the action camera with the one of the GPS, as described in section 2.
4). This way, we infer the location of each picture, store the latitude and longitude in the headers of jpeg files and obtain geolocated pictures which can be displayed on a map (see Figure  9 ). A the end of the workflow, some of the exif metadata stored in the headers of JPG files have been extracted and stored with the deployment metadata in a PostgreSQL/Postgis database wich can be used to filter images and store tags (in particular labels to train neural networks). Amon possible metadata elements we extracted the following ones: SourceFile, FileName, Directory, GPSLatitude, GPSLongitude, GPSPosition, GPSDateTime, DateTimeOriginal, LightValue, ImageSize, Model.
A workflow for data management is required to ensure that a collaborative approach will be sustainable and will enable to properly manage the life cycle of pictures and other observations which will be collected in the long term. A first workflow has been set up by using open source codes and softwares. However we need to find simple ways to get new citizen scientists onboard and we still face multiple challenges to equip platforms, simplify data transmission and customize sensors. One of the mshort term challenge consists in improving the accuracy of GPS location (using Real Time Kinematic, RTK) and in managing the angle of pictures taken from the kite (Inertial Measurement Unit, IMU). This will require some work to customize the camera with additional low cost components. In addition we plan to add some additional sensors (like fins from smartfin project to collect environmental parameters).
The southern Levant is a Mediterranean climate zone of complex variability in which uncertainty remains in regional palaeoclimate reconstruction. In spite of the proven value of diatoms in circum-Mediterranean palaeoenvironmental research, their potential remains largely unexplored in the southern Levant region. In this study, we generate a new, highresolution multi-proxy record for the last ca 9,000 cal yrs BP, supported by diatom data and key biological, mineralogical and geochemical indicators preserved in a 17. 8 m long sediment sequence recovered from Lake Kinneret (the Sea of Galilee), Israel. During the Holocene, well-correlated shifts in the diatom, minero-geochemical and palynological data indicate marked lake-level variation over time as well as changes in the trophic state of Lake Kinneret.
Therefore supporting evidence for lake-level change, coupled with regional shifts in moisture availability, is sought from the comparison with known palynological datasets and climate models as well as speleothem data from other study sites. Known trophic preferences of diatom taxa and the geochemistry data can help to disentangle if changes in the diatom assemblage reflect fluctuating lake-level conditions or are the result of productivity shifts. The ratio of planktonic to benthic diatoms was calculated using the following formula provided by  Wang et al. (2013) : 𝑃 𝐵 = ∑(planktonic taxa) ∑(planktonic + benthic taxa) Following the allocation of  Pollingher et al. (1984) , facultative planktonic taxa such as Pseudostaurosira brevistriata, Staurosira venter and Staurosirella pinnata and epiphytic species such as Cocconeis spp.
, 1996)  and X'Pert High Score Plus (PANanlytical B. V. ). The Rietveld refinement was applied using Profex v. 3. 10. 2 (Döbelin and Kleeberg, 2015)  with additional structure files from the American Mineralogist Crystal Structure Database  (Angel et al. , 1990 , Bailey, 1969 , Maslen et al. , 1995). Compositional data analysis was applied to the geochemical data  (Aitchison, 2003, Comas-Cufí and Thió-Henestrosa, 2011)  and gives further insights to the inter-element and possible source relationship  (Aitchison and Greenacre, 2002). Biplots of complex data-sets can be used as a simple tool to investigate the structure of these data-sets  (Greenacre, 2010).
The detrital input can increase: (a) with enhanced humidity/rainfall, (b) with a lower lake level due to:(1) a reduction in lake water volume also reduces internal biomass and calcium production, which can cause a relative increase in detrital material, and (2) because a shallow lake exposes sediment surfaces in shallower littoral zones; (c) with open vegetation cover on the surrounding landscape, which increases sediment and nutrient input to the lake system, whereas a dense vegetation cover stabilizes the soil. [insert figure  5 ] Discussion: In the following sections we discuss major palaeolimnological and environmental changes inferred from diatom analysis and minero-geochemistry data of a 17. 8 m long sediment sequence of Lake Kinneret. The study spans the last 9,000 cal yrs BP and emphasis is given to disentangling climate-driven lake-level variation from shifts in trophic status and the impact of past human activities.
, 2013 (Langgut et al. , , 2015) )  seem not to have a remarkable effect on the lake's ecosystem and trophic state, as interpreted from the high-resolution diatom record. In summary, all analysed proxies indicate a stable, oligo-mesotrophic lake system for the mid-Holocene with a maximum lake-level high-stand lasting from 7,500 till 2,200 yrs cal BP. This is in accord with climate models based on palynological data, providing evidence for an extended humid phase with precipitation values up to 650 mm/a for the Levantine region  (Litt et al. , 2012). Additionally, our dataset is in accord with other diatom records in the Eastern Mediterranean, which also exhibit an inferred mid-Holocene phase of maximum lake levels (e. g. Lake Ioannina;  (Jones et al. , 2013) ; Lake Prespa  (Cvetkoska et al.
Sediment disturbance at 2. 3 k cal yrs BP In the rather homogenous Holocene sediment sequence of Lake Kinneret, a major shift in palaeolimnological proxy data at 4. 64 to 4. 57 m depth (ca. 2. 3 cal k yrs BP; figure  7 ), indicates the occurrence of an event layer. This 4 cm thick sediment sequence shows abrupt lithologic and mineralogical boundaries, and grain size shows inverse grading, which is indicative of a rapid depositional event. Concentrations of microcline, detrital dolomite and quartz are the highest of the profile and potassium and phytolith counts exhibit clear peaks. This distal deposit of a turbidity current/gravity flow might have originated from two distinct natural triggers (a) seismic activity or (b) climate, as a result of a flash flood event. Terrestrial deposits of paleo-earthquakes with Holocene age are encountered at the south-eastern margin of Lake Kinneret (e.
, 1984). The geochemical data exhibit an increase in detrital values and maximum pyrite values. The lack of shifts in other indicators compared to DAZ-1b suggest, that it was only recently that the annual pattern of lake mixing was established. This is also supported by the laminated sediment deposits only occurring in the upper most 25 cm of the sediment sequence. Lake levels fluctuated markedly from 1,600 cal yrs BP until present, as indicated by shifts both in diatom concentration and the P/B-ratio. As noted above, lake levels can fluctuate by up to 4 m per year depending on precipitation/evaporation, but mostly on human water management control. Human-induced impacts (e. g. water abstraction, industry and agriculture) on the lake ecosystem and its watershed are well documented over the last > 40 years by the Lake Kinneret monitoring program  (Sukenik et al.
Conclusions: Our study has greatly improved our understanding of Holocene climate variability in the Lake Kinneret area and across the southern Levant as a whole. The diatom data in particular provide a robust signal of lake-level response to shifts in moisture availability, although partly obscured in the later record by the impact of human activities. In the context of understanding palaeoclimate variability our major conclusions are: 1. Apart from lithological evidence for a possible disturbance event around 2,300 cal yrs BP, the Lake Kinneret sediment record provides an important continuous highresolution Holocene sequence for the southern Levant. 2. Major shifts in the diatom community and especially in the P/B-ratio, during the Early and mid-Holocene are driven by changes in lake level and moisture availability rather than lake productivity or changes in trophic status.
Acknowledgements A part of this work (preliminary investigations) is based on the unpublished master thesis of  Hannah Vossel  carried out at the  Steinmann Institute at the University of Bonn  under the supervision of  Thomas Litt  and  Georg Heumann. We would like to thank  Vera Schiebel  for providing palynological datasets and  Andrea Miebach  and  Mordechai Stein  for the critical discussion and comments. We also would like to thank  Christoph Steinhoff  and  Helen Böttcher  for preparing samples for further laboratory procedure. Last but not least, we would like to thank  Georg Heumann ,  Sven Oliver Franz  and  Michael Köhler  for their support during the coring campaign and two anonymous reviewers for the constructive comments on our manuscript. Funding Part of this research, including the drilling campaign, was funded by the  German Research Council (DFG)  as part of the  Collaborative Research Center CRC 806 "Our Way  to  Europe".
This study presents a revised river routing scheme (RRS) for the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model. The revision is carried out to benefit from the high resolution topography provided the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS), processed to a resolution of approximately 1 kilometer. The RRS scheme of the ORCHIDEE uses a unit-to-unit routing concept which allows to preserve as much of the hydrological information of the HydroSHEDS as the user requires. The evaluation focuses on 12 rivers of contrasted size and climate which contribute freshwater to the Mediterranean Sea. First, the numerical aspect of the new RRS is investigated, to identify the practical configuration offering the best trade-off between computational cost and simulation quality for ensuing validations.
Second, the performance of the revised scheme is evaluated against observations at both monthly and daily timescales. The new RRS captures satisfactorily the seasonal variability of river discharges, although important biases come from the water budget simulated by the ORCHIDEE model. The results highlight that realistic streamflow simulations require accurate precipitation forcing data and a precise river catchment description over a wide range of scales, as permitted by the new RRS. Detailed analyses at the daily timescale show promising performances of this high resolution RRS for replicating river flow variation at various frequencies. Eventually, this RRS is well adapted for further developments in the ORCHIDEE land surface model to assess anthropogenic impacts on river processes (e. g.
The corresponding observed discharge was gathered from 3 sources: (1) the daily and monthly river discharge time series from the Global Runoff Data Centre (GRDC, D-56002 Koblenz, Germany); (2) the daily discharge from the French national hydrometry portal (La Banque Hydro, http://www. hydro. eaufrance. fr); (3) the daily discharge for Italian rivers (Po and Tiber) from the corresponding Italian database (personal communication by Dr. Luca Brocca, CNR-IRPI, Italy). Only seven stations provide daily discharge values. For comparison to these observations, which exhibit some data gaps (less than 15% of the observational record but for three stations, cf. Table  1 ), the simulated discharge values corresponding to the missing values are eliminated. This is done to construct the studied hydrographs, as well as the derived validation metrics.
Following  Moriasi et al. (2007) , we used the following metrics: the Pearson correlation coefficient (CC), Nash-Sutcliffe coefficient (NS), and normalized standard deviation (NSD, normalized by the observed standard deviation), which indicate good performances when reaching 1; the percent bias (PCBIAS, normalized by the observed mean discharge), the ratio of the Root Mean Square Error to the observation standard deviation (RSR), and the cross-correlation lag time (CLT), which indicate good performances when approaching 0. The CLT is defined as the lag in in days needed to have the maximum correlation between the simulated and observed series. They are numbered from 1 to 12, and the corresponding names are given in Table  1. The river network is plotted in blue based on the dataset from the Generic Mapping Tools (http://gmt.
Numerical design The RRS is integrated in the ORCHIDEE land surface model  (Krinner et al. , 2005) , where the surface water budget and the resulting surface and sub-surface runoff fluxes are estimated by a multi-layer soil hydrology scheme  (De Rosnay et al. , 2002). In this study, the model is forced by atmospheric forcing data, and three datasets are available, all based on the Watch Forcing ERA-Interim dataset  (Weedon et al. , 2014). They differ by the precipitation data set they are combined with to obtain biascorrected precipitation: (1) the CRU dataset  (WFDEI_CRU, Harris et al. , 2014) ; (2) the GPCCv5 dataset (WFDEI_GPCC,  Schneider et al. , 2014) ; (3) the MSWEP dataset (WFDEI_MSWEP,  Beck et al. , 2017). The first two forcing data sets are at 1/2 • spatial resolution, and the last one is at 1/4 •.
10 The time step of ORCHIDEE is 30 minutes, and the one of the RRS is 1 hour, which is compatible with the above spatial resolutions based on previous theoretical experiments (not shown). MQ: Monthly mean discharge (m 3. s -  ). LQ: Minimum value of monthly discharge (m 3. s -  ). HQ: Maximum value of monthly discharge (m 3. s -  ). SD: Standard deviation of monthly discharge series (m 3. s -  ). a, b, c: Observation data source is from La Banque Hydro, the GRDC and Dr. Luca Brocca (CNR-IRPI), respectively. 3 River routing scheme.