dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, we own two kinds of collected data related to the customers of this supermarket: receipts and individual trips. Receipts were collected during twenty-eight days. Each week, the store registers around 35,000 transactions representing about 285,000 items purchased per week (220,000 receipts). Individual trips of customers were also recorded during the same period. Identification tracking (PathTracker R ) tags based on Radio Frequency IDentification (RFID) technology  [41]  were placed on approximately 250 shopping carts and baskets. 1,500 antennae were located around the perimeter of the store to detect signals transmitted by tags at every second. An antenna covers an area of about 1. 6m 2. A server captures the coordinates of tags with regard to the wireless antennae. Each sequence of coordinates represents the path for each customer as he/she travels throughout the store.
Clustering on trajectories As is traditionally done in machine learning, our set of trajectories has been split in two: 1,038 trajectories were used for learning clusters, the rest was used to validate the results. Many techniques have been proposed in literature for mining trajectory data  [50]. Since our raw trajectories are time-stamped, it is possible to compute a single matrix in which each column is labelled by a node of the store, and each line is a recorded trajectory  [25]. The clustering algorithm used on the resulting matrix is K-means  [32] , tailored to the use of binary vectors. To fix the number k of clusters, we tested different values between 5 and 60, and our choice is based on the obtained results by analysing cluster variety and compactness (using the tool Silhouette with Matlab 10 ).
Acknowledgments This research was supported by the competitiveness cluster PICOM. It was funded by the  French Ministry of Industry and the region Hauts-de-France. We would like to thank the company  Sorensen Associates  for giving us access to real store data. The authors would like to thank  Christophe Kolski  ( LAMIH, UPHF ),  Sanda Lefteriu  ( IMT Lille Douai ) and  Marius Silaghi  ( Florida Institute of Technology, FL, USA ) for their useful comments on this paper. We thank also the anonymous reviewers for their constructive remarks.
Model design MetaConnect simulates metapopulation dynamics and genetics using the species life cycle and life history traits, the landscape characteristics and their interactions. The simulations allow inferring of individual dispersal and local and global extinction probabilities, genetic diversity and genetic differentiation (from classical Fst analyses or as input files for the Structure software  (Pritchard, Stephens & Donnelly 2000) ). MetaConnect is an individual-and process-based model which means that: • all individuals in the model are independent and behave in respect to their phenotype. • patterns emerging in the different outputs of the model are the products of flexible and adjustable rules implemented in the model.
With MetaConnect, as shown in our case study (figure  6. A-C), we provide an efficient tool to define habitat patches (nodes of a graph) with predictions of the demographic module and dispersal functionality between patches (graph's edges) with dispersal assessed from dispersal simulation or derived from genetic estimates applicable from local to national scales and grid-based data. However, further development of MetaConnect and the development of a specific toolbox will allow that the graph does not model the metapopulation functioning as such, but to be used as a powerful analytical tool preventing the graph-theoretic connectivity analysis from biases described by  Moilanen (2011). We conclude by highlighting the recent call for a new generation of models that begin to provide predictive systems ecology  (Evans, Norris & Benton 2012; Evans et al.
We believe that MetaConnect, together with the recently published software RangeShifter  (Bocedi et al. 2014 ) and the integrative statistical inferences procedure proposed by  Pagel & Schurr (2012) , begin to address this challenge to develop complex systems models that can be used to inform policy in the sphere of spatial and landscape ecology. Taylor, P. D. ,  Fahrig, L. & With, K. A. (2006)  Landscape connectivity: a return to the basics.
Acknowledgement: SM, EB, JC and MB were supported by the  European  project  SCALES  funded by the  European Commission as Large-scale   Integrating Project  within  FP7  under grant  226852. JC, JT, SP and MB were supported by  EU   FP6 Biodiversa ERANET TenLamas. JT and SP were supported by  NERC. SM and MM were supported by the  TerrOïko's R&D internal program. This work is part of the  "Laboratoire d'Excellence" TULIP  ( ANR-10-LABX-41 ). Where to find MetaConnect MetaConnect (IDDN. FR. 001. 430001. 000. S. C. 2012. 000. 20600 ) is developed in C++ by SM and MM and runs exclusively in cloud-computing under  UNIX. A Graphical User Interface (GUI)  developed in OpenGL insures the access to MetaConnect from multiple platforms (currently available for Linux Centos7). MetaConnect is available for academic purposes contacting the authors (SM).
Catchment dataset Several hydrographic regions of mainland France are influenced by large aquifers, which bear the memory of past climatic events and have a significant contribution to river flow. In the Paris basin, the chalk aquifer -composed of late Cretaceous formations -has a significant connection with many rivers in the Seine and the Somme basins, which notably led to major groundwater-induced floods after the extremely wet years of 1999 and 2000  (Pinault et al. , 2005; Habets et al. , 2010). The Loire basin is linked to the Beauce aquifer, mostly made of tertiary layers, whose situation under a large agricultural plain induces water management issues that affect Loire low flows. The international Rhineland aquifer, part of which extends to Alsace, also plays a major role in the hydrology of the Rhine.
Tests were performed on a set of 1664 catchments that were selected on the basis of diversity of area, climate, hydrological regime and completeness of the time series in the period 1967-2018. Streamflow data are from the national database Banque Hydro  (SCHAPI, 2015; Leleu et al. , 2014) , where measurements regarded as unreliable were converted into missing values. Climatic data are from the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie; Analysis System for Providing Atmospheric Information Relevant to Snow) reanalysis  (Vidal et al. , 2010) , developed by Météo France and aggregated for each catchment, to get a lumped climatic series of rainfall and PET. The combination of the SAFRAN and Banque Hydro data is stored in the HydroSafran database, maintained and hosted by INRAE (French National Research Institute for Agriculture, Food and the Environment;  Delaigue et al.
, 2017; Coron et al. , 2020). Tables  2  and 3  show some characteristics of the catchment dataset: it is representative of the climatic and landscape diversity in mainland France. We chose basins with a large range in size, from very small (less than 2 km 2 ) to very large (117 000 km 2 ). A quick geological analysis of the 1664 catchments was also performed based on the national hydrogeological map by  Margat (1980). It highlighted that 1310 catchments are not very likely to be influenced by aquifers, since they are on impermeable formations, such as igneous or metamorphic rocks or clay; 122 catchments have more than 90 % of their surface on sedimentary formations that are likely to carry large aquifers; and 232 catchments have a mixed configuration. Figure  2  shows the geographic repartition of the catchment set.
An ensemble view of the hydrograph shows that long oscillations seem to influence total flow, with a period greater than 1 decade; it is well taken into account by baseflow separation. Among the dataset of 1664 catchments, optimisation of parameter S failed for three catchments only; there, the correlation criterion kept increasing with the value of S, without the possibility of reaching an optimal value. The failing catchments are small basins where most of the water comes from glaciers in the Alps: effective rainfall -as computed by the Turc-Mezentsev formula -is thus not really correlated with baseflow, and the quadratic reservoir fails to reproduce the particular memory effect of a glacier.
Split-sample test as a stability assessment In order to evaluate the stability of the proposed algorithm and of its calibration procedure, a split-sample test was performed. The time extent of the time series of the dataset -1 August 1958 to 31 July 2018 -was divided into two equal periods P 1 (1 August 1958 to 31 July 1988) and P 2 (1 August 1988 to 31 July 2018). The algorithm was then calibrated separately for each basin on these two periods, and the values obtained for BFI and τ were then compared. Figure  10  shows the graphical comparison of obtained BFI and τ values. Table  5  displays two statistical assessments that were performed after the split-sample test: correlation between values of BFI and τ and the non-parametric Kolmogorov-Smirnov test  (Smirnov, 1939) , in order to check the equality of distributions, i.
Synthesis The hydrograph separation algorithm and its calibration procedure presented in this work have yielded credible results for a large set of test catchments. The algorithm has suitable characteristics for such a model: (i) frugality -only two parameters to calibrate; (ii) objectivity -the procedure is not supposed to require any intervention or interpretation from the user; (iii) generalisation ability -the procedure succeeded on the whole dataset, with a unique value found for the reservoir parameter; and (iv) repeatability -as assessed by the split-sample test performed on the test dataset. The analy-Hydrol. Earth Syst. Sci. ,  24, [1171] [1172] [1173] [1174] [1175] [1176] [1177] [1178] [1179] [1180] [1181] [1182] [1183] [1184] [1185] [1186] [1187] 2020  www. hydrol-earth-syst-sci.
Limitations We used an optimisation procedure set which searches for values of τ less than 5 years. It showed great convergence ability, i. e. an optimal point was found for every catchment of the test dataset. Yet, a mere glance at the Petit Thérain River hydrograph (Fig. 7 ) shows that its response time may be greater than 5 years, with long dynamics at the scale of climatic cycles. All this highlights the fact that the simplistic distinction between baseflow and quickflow hides the diversity of timescales and travel times among processes that influence the total flow of a river. Anyhow, the optimisation criterion is not indisputably univocal for a significant number of catchments; therefore, more work is needed to conceive a better optimisation criterion for the parametrisation strategy of the algorithm.
The split-sample test is a way to check temporal consistency of the baseflow separation method; the one performed on the test dataset can be considered as successful, at least concerning BFI results. Nevertheless, the spatial coherence of the method was not checked; it could be tested, for instance, with the propagation of baseflow sub-basins of a larger catchment, in order to test whether the sum of tributary baseflows makes up total baseflow. Our optimisation  procedure does not take into account regional aspects while calibrating the model, although it could be a way of introducing objective prior knowledge into the method. On a sample of catchments where chemicophysical data are available, a comparison with tracer-based separation procedures would be a reliable way to validate the presented method.
Implementation An R package named baseflow  (Pelletier et al. , 2020)  is provided to ensure the reproducibility of our results. It implements the algorithm presented in this paper with a highperformance computing core written in Rust. The example data taken from airGR are linked in the examples. The package is being released on CRAN (Comprehensive R Archive Network), and its documentation is available as supplementary material. Data availability. Data for this work were provided by Météo France (climatic data) and the hydrometric services of the French government (flow data). Flow data is publicly available on the Banque Hydro website http://hydro. eaufrance. fr/ (SCHAPI, 2015). Climatic data is not publicly available, since it is a commercial product; it can be freely provided -for research and non-profit purposes -upon request to Météo France.
Acknowledgements. Data for this work were provided by  Météo France  (climatic data) and the hydrometric services of the French government (flow data). We would like to thank  Pierre Nicolle  ( Université Gustave Eiffel ) and  Benoît Génot  ( INRAE Antony ) for the database creation and maintenance and  José Tunqui Neira  ( Sorbonne Université ) for his help in the literature review. We would also like to extend our warmest thanks to  Charles Perrin  ( IN-RAE Antony ),  Lionel Berthet  (DGPR) and  Paul Astagneau  ( INRAE Antony ) for their remarks and suggestions during the writing process. Last but not least, computing codes could not have been developed without the precious expertise and availability of  Olivier Delaigue  ( INRAE Antony ) and  Denis Merigoux  ( INRIA Paris ). Review statement.
It is necessary to obtain a large database of images covering the common species in order to train the network. Out of the modern living 400 to 500 polycystine species, about 100 are relatively common  (Boltovskoy, 1998) , however, they have yet to be imaged to create a database for automated recognition purposes. Some online Cenozoic radiolarian databases have already existed for a few years (e. g. WoRaD,  Boltovskoy et al. , 2010; radiolaria. org, Dolven and Skjerpen, 2006; Radworld, Caulet et al. , 2006; see Lazarus et al. , 2015  for an extensive review of the existing databases), however these are more directed to creating a catalogue for taxonomic purposes. As such, we created an exhaustive and participative database specifically for CNN training and automated recognition purposes, called AutoRadio (Automated Radiolarian visible at: https://autoradio.
Material Radiolarian microfossils to be used in this study were extracted from several sediment cores. Core MD97-2140 was retrieved from the centre of the West Pacific Warm Pool (WPWP; latitude: 2 • 02'N; longitude: 141 • 46'E) at a water depth of 2547 m during the Marion Dufresne IMAGES III-IPHIS cruise in 1997  (Beaufort et al. , 1997). This core is currently stored at the CEREGE laboratory, France. The sediments consist of a greyish and compact calcareous nannofossil ooze, also containing abundant radiolarian and foraminiferal faunas (de  Garidel-Thoron et al. , 2005). Several samples were chosen to extract siliceous microfossils and thus construct a radiolarian images database. Their depths within the recovered core are: 3-4 cm, (6. 3 ka de  Garidel-Thoron et al. , 2005), 48-49 cm (11.
8 ka), 82-83 cm (16. 4 ka), 98-99 cm (18. 8 ka), 245-246 cm (38. 0 ka), 363-364 cm (53. 3 ka) , 405-406 cm (63. 0 ka), 417-418 cm (67. 8 ka), 487-488 cm (77. 7 ka), 648-650 cm (120. 4 ka), 727-728 cm (141. 4 ka). For detail on sample processing and slide preparation, the reader is referred to section 2. 2. A few other Miocene to recent samples retrieved from the Warm Pool were also used later to increase the number of rare and absent species in the database. These cores were also taken during the Marion Dufresne IMAGES III-IPHIS cruise: Core  MD97-2138 MD97- (1760 MD97- -1761 MD97- cm, 2670 MD97- -2671 cm, 3151-3152 cm) cm, 3151-3152 cm) ; and from IODP Expedition 363  (Rosenthal et al. , 2018) : Holes U1483A (samples from sections 9H-4W, 14H-5W), U1483B (6H-6W, 18H-2W), U1486B (3H-3W, 6H-4W, 13H-4W), and U1486C (21H-4W).
Random Settling Protocol A new protocol was developed as a proposed standard methodology for preparing radiolarian microscopic slides. It places 8 samples per standard 76x28 mm slide using 12x12 mm cover slides on which radiolarians are randomly and uniformly decanted using a new 3D-printed decanter (Fig. 1a-b ). The 3D file for this new decanter was designed online, using the Autodesk, Inc. 3D design platform Tinkercad (https://www. tinkercad. com/), and is available for free at https://github. com/microfossil/Decanter. It was printed on a Raise3D fused deposition layer type printer using 1. 75 mm R3D Premium PLA filament for a material cost of about 1 euro. Approximately 30 g of filament was used, and 4. 5 hours were needed to print the model using a standard resolution layer height of at least 0.
Automated Image acquisition Particular emphasis was placed on acquiring high-quality slide images, as being able to recognise different radiolarian species depends on having clearly visible features. For each radiolarian microscopic slide, the 8 cover slides (corresponding to 8 samples) are automatically and consecutively imaged using a Leica DMR 6000 B automated transmitted light microscope (200x magnification using a HCX PL FLUOTAR 20 x magnification Leica lens) and a Hamamatsu ORCA Flash4. 0 LT camera, controlled via a LabVIEW (National Instruments) interface. The microscope parameters were set as: Intensity: 10; Depth of field: 38; Aperture: 33; and the condenser was lowered by 9 mm from the glass slide. The LabVIEW acquisition software parameters were set as: Exposure: 9 ms; Gain: 1. These settings provided the maximum contrast between the glass shells and their mounting medium.
Automated Image Processing and Segmentation Image processing and segmentation is performed via a second LabVIEW interface. For each FOV, the batch of 15 images is pattern (Fig. 2 ). Every stacked FOV image is then processed and segmented into individual specimen images using a custom plugin (AutoRadio_Segmenter. ijm) developed for the ImageJ / Fiji software (V1. 52n Schneider et al. , 2012). The processing steps are: 1. Open a stacked FOV image. 2. Subtract its background. 3. Adjust the minimum and maximum greyscale value to increase its contrast. 4. Invert the image and create a mask. 5. Threshold it in order to binarise it. 6. Blur it and threshold it again to obtain the overall shape of each particle. 7. Separate particles that are in contact with each other (require the configurable Biovoxxel "Water Irregular Features" plugin, available at: https://github.
Database building and CNN training ParticleTrieur is a dedicated software program developed at CEREGE  (Marchant et al. , submitted) , that enables the operator to visualise and assign vignettes to manually defined classes, and uses the k-NN (k-nearest neighbours) algorithm to aid in identification by self-learning and progressively suggesting identification once enough radiolarian pictures are identified. Using this software, a large dataset of radiolarian taxa images (called the AutoRadio Database) was progressively built (the original version of the database used in this study can be downloaded at: http://microautomate. cerege. fr/dat). It is currently composed of 17,065 images, corresponding to 112 classes/taxa. Each class contains between 1 and about 1,000 images. Once labelled, this database was used to train a CNN (convolutional neural network) for the automated taxonomical identification of radiolarian vignettes resulting from the automated microscope image acquisition, processing, and segmentation steps (Fig.
This means that a few species were regrouped into a single class when a significant morphological gradation was observed and when the limit between the considered species was blurry (e. g. , Eucyrtidium acuminatum and E. hexagonatum; Sithocampe arachnea and S. lineata; Actinomma henningsmoeni and A. leptodermum). All manual taxonomic IDs during the building of the database were reviewed by a radiolarian taxonomy expert (G. Cortese) to ensure consistent and accurate identifications.
5: Automated radiolarian recognition using a CNN, as well as calculating morphometric measurements. 6: Automated export of census data per sample (usually about 500 radiolarian images per sample), and storage of radiolarian images in folders corresponding to their taxonomic identification for every sample. The whole procedure is then entirely automated from the image acquisition to the census counts, and only required from the operator to prepare the micropalaeontological slides and put them under the microscope. The operative workflow described in this study can thus perform complex, tedious, time-consuming tasks such as taxonomic identification and census counts by producing reliable, reproducible, and accurate results. This workflow is achieved using a polyvalent and extensive radiolarian image database (currently 17,065 images) and a ResNet CNN trained using transfer learning for modern and Neogene radiolarian identification.
The CNN is currently able to recognise 84 classes with an average precision of about 90 %, an overall score that was also obtained on a test performed on 8 random samples containing more than 10,000 images. In order to continue to increase its efficiency, more images are required, particularly so for rare species. To this aim, the database was made free-access and participative to increase the number of images, especially for rare species where the recall score is relatively low, most likely due to low numbers of training images for these taxa. This new workflow and associated CNN has the potential to make paleoclimate studies more approachable and feasible, along with biostratigraphy for very long sequences. The radiolarian census data can then be used to investigate the radiolarian assemblages variability for biostratigraphical purposes, and to develop, apply and improve existing assemblage-based palaeoenvironmental proxies such as SSTs (e.
g. , radiolarian-based palaeotemperatures for the late Quaternary,  Cortese and Abelmann, 2002; Subtropical (ST) Index, Lüer et al. , 2008; Radiolarian Temperature Index (RTI) , applied to Miocene samples,  Kamikuri, 2017; and paleoproductivity (e. g. , Upwelling  Radiolarian Index (URI),  Caulet et al. , 1992 ; Water Depth Ecology index (WADE),  Lazarus et al. , 2006). It also enables the investigation of evolutionary trends, appearance of new species, and rate of evolutionary change, a fascinating topic regarding radiolarians and other microfossil groups. This dataset and following studies also enable the fast and accurate measurement of numerous morphometric parameters for each vignette that was assigned a class in the automated recognition step. In addition to the previous research applications, the morphometry aspect provides the possibility to investigate the link between the morphological variability of a species or an assemblage through time along a sedimentary record and elaborate/test scenarios to explain such variability.
/doi. org/10. 5194/cp-2020-76 Preprint. Discussion started: 9 July 2020 c Author(s) 2020. CC BY 4. 0 License. 3 Results and discussion 3. 1 Description of the database Of the 17,065 images used to construct the database, 112 morphoclasses were created. Of all these classes, 104 belong to.
Acknowledgements. We thanks  IODP-France  for financial support for this project. This work was also supported by the  French National 380 Research Agency (ANR)  as part of the  French platform called Nano-ID  ( EQUIPEX  project  ANR-10-EQPX-39-01 ) and the  ANR  project  FIRST  ( ANR-15-CE4-0006-01 ). We also thanks the program Ocean Acidification from the  french Foundation for Research on Biodiversity (FRB) , and the  Ministry for the Ecological and Inclusive Transition (MTES)  in supporting the project  COCCACE. 19 https://doi. org/10. 5194/cp-2020-76Preprint. Discussion started: 9 July 2020 c Author(s)  2020. CC BY 4. 0 License.