dataesr/scientific-paragraphs-categorization huggingface dataset:
Modelling of current and past ranges Current and Last Glacial Maximum (21k years ago; LGM) potential distributions were modelled using  MaxEnt (v3. 3. 3; Phillips et al. 2006)  as implemented in 'biomod2  ' (v3. 3-7. 1; Thuiller et al. 2009 ). Current and LGM (MIROC global circulation model) climatic data were downloaded from WordClim ver. 1. 4  (Hijmans et al. 2005 ) at a resolution of 10*10 arc-minute. The LGM period represents the latest unfavourable climate for tropical species and is therefore a good period to model the impact of past climate change on potential range. A total of 346 presence data points (Table  S1 ) covering the known distribution of A. affinis were spatially filtered to one point per cell to avoid overfitting due to sampling bias. Model overfitting was constrained by using the regularization parameter in Maxent, which limits model complexity  (Radosavljevic and Anderson 2014) , and was set to 2.
We compared these models using the R package 'ENMTools'  (Warren et al. 2019). Models were built using the generalized linear model (GLM) approach using the same bioclimatic variables that were used in our MaxEnt models above. We performed niche equivalency tests (100 replicates) using the metrics Schoener's D  (Schoener 1968 ), I  (Warren et al. 2008 ) to assess whether ecological niches of individuals North and South of the climatic inversion were more different than expected by chance. We also performed symmetric background tests (100 replicates;  Warren et al. 2008) , to correct for habitat availability. If observed values of D or I are significantly different than the simulated distribution we can state whether the observed similarity between populations is significantly larger or smaller than expected in the context of the available environment.
Sequencing A total of 124. 7 million reads were generated for 112 A. affinis individuals at an average coverage depth of 77. 5x across all targeted loci. Using HybPiper we identified 366 loci where 75% of the exon length was recovered in at least 75% of individuals. A total of 15 loci showed signs of paralogy and were removed, leading to a final dataset of 351 supercontigs totalling 756 kb of sequence data. After cleaning and filtering our SNP calling approach yielded 5,964 high-quality SNPs from 262 different loci. A comparison of the output of the SeCaPr and HybPipier pipeline include amount of genetic variation and length of sequence recovered can be found in Table  S2. We found 240 loci that were reconstructed in both pipelines, representing 78% of the total number of SeCaPr loci (306) and 68% of HybPiper loci (351).
1B, S3 ) with geographic discontinuities generally congruent between analyses (Fig. 1 ; Fig. S4 ). Individual admixture proportions revealed generally limited mixed ancestry within samples (Fig. 1B ), though we detected a conspicuous amount at a single location. The two northern most individuals belonging to cluster EG, found in at Meyo Centre in Cameroon (labelled in Fig. 1A ), had a considerable proportion of their ancestry from cluster CA (Fig. 1B ). The inverse was true of the two individuals from cluster CA that were from the same location. The fastSTRUCTURE analysis was similar to the aforementioned analyses, even with a reduced SNP dataset, though the grouping of individuals belonging to DAPC clusters GC and EG was not consistent for different values of K (Fig. S5 ). However, there was little evidence for admixture when using this approach including for "Meyo Centre" individuals.
Which areas have remained climatically stable over time? A total of 113 data points were retained after filtration. The best predictors were Precipitation of Wettest Month (Bio13) and Precipitation Seasonality (Bio15) (Table  S3 ). A regularization multiplier of 2 generated a better model fit than with 4, showing a good visual match with the known distribution of the species at present (Fig. 4A ). The mean value of the AUC for the training and test data were respectively 0. 77 and 0. 76. The mean value of TSS was 0. 454, indicating that the model is better than a random model. During the LGM, the highest presence probabilities were all located along the Atlantic coast in Cameroon, Equatorial Guinea and Gabon (Fig. 4B ).
ACKNOWLEDGEMENTS We thank  Prof. Moutsambote ,  Raoul Niangadouma ,  Théophile Ayole  for help in the field. The authors acknowledge the  IRD itrop HPC (South Green Platform) at IRD Montpellier  for providing HPC resources. We are grateful to the  Centre National de la Recherche Scientifique et Technique (CENAREST) , the  Agence National des Parques Nationaux (ANPN)  and  Prof. Bourobou Bourobou  for research permits ( AR0020/16 ;  AR0036/15  (CENAREST) and AE16014 (ANPN). Fieldwork in Cameroon was undertaken under the "accord cadre de cooperation" between the  IRD and Ministère de la Recherche Scientifique et Technique.
Computational Fluid Dynamics Ship-Iowa: IIHR, The University of Iowa. Numerical simulations provided by IIHR, The University of Iowa, have been made by the in-house CFDShip-Iowa V4. 5, which is an incompressible URANS/DES solver  [36]  for ship hydrodynamics applications. Several turbulence models have been implemented in the solver, including the isotropic blended ke/kx, the anisotropic EARSM, the kx-SST model, and DES approaches with near-wall models. For the air/water interface, CFDShip-Iowa V4. 5 uses a single-phase level set approach. The code has the capability of running full six degrees-of-freedom (DOFs) simulations. The numerical methods are based on a finite difference discretization on body-fitted curvilinear grids; viscous terms are discretized by a second-order centered schemes, whereas higher-order upwind schemes have been used for the convection terms.
Time integration is achieved by a second-order backward difference Euler scheme. High-performance parallel computing capabilities are obtained by a message passing interface-based domain decomposition approach; each decomposed block is mapped to one processor. The overset domain connectivity information which is used to connect the solution between overlapping grids is obtained using the code SUGGAR  [37]. For the computations around the catamaran, a rectangular computational domain has been used, with extensions [0. 4 L pp , 2. 6 L pp ], [À0. 7 L pp , 0. 7 L pp ], and [1. 3 L pp , 1. 3 L pp ] in the streamwise, spanwise, and vertical directions, respectively. No-slip boundary conditions are enforced on the catamaran surfaces, whereas far-field boundary conditions are imposed on the top, the bottom, and the sides of the domain; at the inflow section, inlet boundary condition is used with zero pressure gradient.
Comparison in Fig. 12  shows that all the submissions provide similar results for the vortex core position. As it is also shown in Table  20 , differences against EFD are rather low for the first three planes (8% maximum for both lateral and vertical positions among all the submissions); the errors increase moving downstream (up 20-25% for the lateral position, even larger for the vertical position). However, since differences between CFD are negligible (see top panels in Fig. 12 ), large comparison errors in the last two planes seem due to uncertainty on the EFD data (the position of the catamaran) rather than inaccuracies in the CFD predictions. Axial vorticity variation along vortex core for PT-FKV is shown in Fig. 12 ; error with respect to EFD is reported in Table  20. Large differences are seen between CFD submissions; very large comparison errors are seen for all submissions against EFD data.
7% to 56. 1%. These observations confirm that, at the onset, differences among submissions are mainly due to different grid refinement. Moving downstream, the vorticity starts to decay; generally, the differences between CFD predictions decrease (in absolute value as distance from the mean), whereas the errors with respect to EFD increase (with comparison errors up to 90%). Data do not allow to highlight clearly any grid or turbulence model dependency. Similar behavior is seen for the prediction of the ST-FKV (not reported here, see Ref. [11] ), with errors generally lower than PT-FKV. TKE predictions are much smaller than experiments (at least one order of magnitude), with an overall error of about 90-100%. Higher values are predicted by those computations performed on more refined grid (IIHR-GO and ISIS-kx-AMR).
Acknowledgment CNR-INM: The research has been partially supported by the  Italian Flagship Project RITMARE , funded by the  Italian Ministry of Education , Research Program 2011-2013 and by the  U. S. Office of Naval research , in the framework of the NICOP projects (managed by  Drs. Patrick Purtell ,  Ki-Han Kim ,  Thomas Fu , and  Woei-Min Lin ) "Complementary EFD and  CFD Analysis of Calm Water Hydrodynamics and Large Amplitude Motion for High-Speed Catamarans ," Grant  N00014-08-1-1037  and " Experimental Study of Static Drift and Stochastic Added Resistance for the Delft Catamaran ," Grant  N62909-12-1-7082. ECN/CNRS: This work was granted access to the  HPC resources of IDRIS  under the allocations 2014-2A1309 and 2015-2A1308 made by  GENCI  ( Grand Equipement National de Calcul Intensif ).
IIHR, The  University of Iowa : This research was sponsored by the  Office of Naval Research  under Grant  N00014-01-1-0073  under the administration of  Dr. Ki-Han Kim  and  Dr. Thomas Fu , whose support is greatly appreciated. Partial support to  Timur Dogan  was provided by the  Naval Engineering Education Center (NEEC)  of the  U. S. Navy  administered by  Professor Steven Ceccio  at the  University of Michigan. The authors wish to thank  Dr. Ki-Han Kim  for his thorough review of the paper.
Goat Movements in Java, Indonesia (2016) In Indonesia, the iSIKHNAS animal health and production information system provides a practical source of detailed individual animal movements which can be used to simulate and evaluate alternative sequences of control activities  (29). Data on all recorded individual goat movements on the island of Java in 2016 was extracted from the iSIKHNAS database, as an origindestination matrix. For the purposes of this example, the district (kabupaten) is used as the population unit, although smaller units would be likely to be used in practice. Of the 119 districts in Java, 60 recorded intra-island goat movements in or out during 2016, with a total of 54,995 animals moved. An exhaustive analysis of all combinations of the 60 districts would require analysis of 8.
Cattle and deer movements in New Zealand (2018) The same approach was used to a dataset consisting of all completed cattle and deer movements in New Zealand in 2018, consisting of movements between 73 cities and districts  (32). In contrast to the Java data, 40. 98% of movements were reciprocal. The distribution of the results for 100,000 random sequences are illustrated in Figure  6. In this case, the minimum number of blocked movements calculated was 40,392,916, which is 43% of the largest number observed. The best sequence observed is illustrated in Figure  7. These two examples demonstrate that, using a random sample, it is feasible to estimate a good eradication sequence that is close to the optimal, even when the number of units is large. It also shows that the movement structure has an influence on the benefits that can be gained from this approach.
Addressing Counter-Current Movements Regardless of the approach taken, optimizing the sequence of disease eradication is unlikely to ever result in a situation where no movements need to be blocked. The risk of producers and traders circumventing movement restrictions may be further managed by the implementation of risk-based strategies to specifically address the small number of remaining movements that would otherwise be blocked. Detailed movement data will allow risk to be assessed in detail, taking multiple factors into account, including the age, sex, and purpose of the animal, production system, and nature of the movement and the nature of the destination (slaughter, market, breeding, fattening). Risk management strategies may include direct compensation, creation of short-term alternative markets (to artificially shift the price-driven movement gradient), or risk-based measures such as allowing movement after vaccination and/or quarantine, although any such strategies should be carefully examined to ensure that it does not have counterproductive or unexpected effect on the movement network.
To gain their support, it is necessary to demonstrate value for money, both in the short term (PPR eradication) and the long term (sustainable improvement of veterinary service capacity to deal with other important disease problems). Some of the key lessons regarding sustainability from Indonesia's iSIKHNAS include: • User-focused design: the system is first and foremost designed to meet the needs of field users, not central decision-makers. • Capacity to manage any disease: the system does not focus on a single disease, and can capture information on any disease of relevance to farmers and field officers. • Do not pay for information: Payment for information undermines user perceptions of the value of the information, erodes data quality, and threatens sustainability. • Integrated information management: integration of multiple data types (disease, population, movement, vaccination, disease control activities etc.
Capturing this data requires census-level participation of farmers, field disease control personnel, extension officers and the broader farmer support networks. It requires fit-for-purpose communication tools and powerful integrated data management capacity. None of this is easy, but the Indonesian iSIKHNAS system (4, 29) provides a model of how it can be achieved. Userfocused surveillance and production information systems have the benefit that they address the broader needs and interests of farmers and other field stakeholders, which go well beyond the requirements of the eradication of a single disease. As a result, a system that is developed to support PPR eradication may be used to sustainably support the control of a full range of other diseases, now and in the future. Implementation of high-coverage, user-focused systems is complex, requiring a blend of sociological, epidemiological and information and communication technology skills.
ACKNOWLEDGMENTS The author would like to acknowledge the  Indonesian Directorate General of Livestock and Animal Health Services ' iSIKHNAS system, the  New Zealand National Animal Identification  and Tracing system, and the  Open Government Data Platform, India , for providing access to the data sets used in this paper. Thanks also to  Séverin Loul  and  Paul Yemgai Kwenkam  for their analysis of surveillance in Cameroon,  Rohan Sadler  from Ausvet for improvements to the R script to calculate blocked movements, and  Evan Sergeant  and  John Edwards , both from Ausvet, for their comments on the manuscript.
(Hakimdavar et al. , 2016)  used the data collected on three full-scale extensive green roofs in New York City (USA) to validate a modeling approach based on the Soil Water Apportioning Method (SWAM). Under a humid continental climate, these monitored drainage areas were comprised between 310 and 80 940 m 2. The three main components of the water balance were measured: rainfall with a weather station, water content with soil moisture and water content reflectometer sensors, and discharge with a custom designed weir placed in the drain of the green roof. (Fassman-Beck et al. , 2013)  assessed several green roofs in Auckland (New Zeland) under sub-tropical climate. Their areas were comprised between 17 and 171 m 2. As the experimental setup was focused 85 on rainfall-runoff relationship, only these components were measured: rainfall with a tipping bucket rain gauge and discharge (deduced from water level) from a water pressure transducer and a customdesigned orifice restricted device.
(Cipolla et al. , 2016)  analyzed runoff from a 60 m 2 extent green roof in Bologna (Italy) characterized by a humid temperate sub-continental climate. Continuous weather data and runoff were especially 90 monitored for modeling development. Runoff was estimated by using an in-pipe flow meters consisting of a runoff chamber with an outlet weir and an ultrasonic sensor (to detect water level). The site was also equipped with a weather station measuring several meteorological variables (rainfall, wind speed, wind direction, relative humidity, atmospheric temperature, …). Although these works were focused on the hydrological behavior of green roofs, few of them have 95 actually monitored the 3 components of the water balance. Rainfall and discharge were generally considered as sufficient to assess their performances.
Rainfall measurement Local rainfall is analysed with the help of an optical disdrometer Campbell Scientific ® PWS100. This device is made of two receivers, which are not aligned with a transmitter generating four laser sheets. By analyzing the signals received from the light refracted by each drop passing through the 40 cm 2 160 sampling area, their size and velocity are estimated. A rain rate can then be derived. Disdrometers are now considered as a reliable rainfall measurement instrument  (Frasson et al. , 2011; Gires et al. , 2016; Thurai et al. , 2011). The device is installed since September 2013 on the roof of the Ecole des Ponts ParisTech building (see Fig. 1 ). This disdrometer and its corresponding data have already been presented in details in a previous data paper  (Gires et al.
298×10 8 m/s) 190 Then it is possible to estimate soil moisture content by analyzing the dielectric constant changes into the soil. The usual relationship between volumetric water content and dielectric constant is known as Topp's Equation  (Topp et al. , 1980). It is adapted to a homogeneous conventional soil: θ = -5. 3×10 -2 + 2. 92 ×10 -2 k a -5. 5 ×10 -4 k a 2 + 4. 3×10 -6 k a 3 (3) 195 Where θ is the volumetric soil water content [m 3. m -  ]. Consequently, an ubiquitous wireless TDR sensor network has been implemented on the ENPC Blue Green Wave to measure both water content and temperature. For this purpose 32 CWS665 wireless TDR sensors (produced by Campbell Scientific ® ) were initially installed. The data were collected by 4.
285 Water content and water level data inside the pipe are collected and stored every night on the HM&Co server in two different files. For this purpose, the Loggernet software produced by Campbell Scientific ® has been used. It supports programming, communication, and data retrieval between data loggers and a PC. Concerning the water level file, each line corresponds to a time step for which the following information is recorded (in each line, these values are separated by a coma):.
305 -Volumetric water content (expressed as k a ) for the 16 TDR sensors -STT_B3: Summary Transfer Time for basis, which is related to the total time required for collecting information from all the sensors that are collected to that base. Water level data inside the storage units have been collected by using the open-source Arduino Uno 310 microcontroller board that works in the offline regime. Data are continuously stored on the 64 MB memory card implemented on the board, and copied manually to the HM&Co server once per week. Data contain the following information (in each line, these values are separated by a space): -Item number -Voltage values for the first storage unit -U1 (in mV).
3 Data availability Conversely to rainfall and discharge, which are measured continuously at the same locations, water content sensors can be moved from a location to another on the BGW. Moreover they were rarely 325 conserved during the night for security reason. Nevertheless, during several months at the beginning of 2018, they were maintained on the same section of the BGW (that showed in Figure  1 ). This time period corresponds to 78 days, from February 19 th to May 7 th 2018. It has been selected to provide water balance components measurements to potential users. This data set is available for download from the following web page  (Versini et al. , 2019) : https://doi. org/10. 5281/zenodo. 3467300 330.
340 In details, the python script is structured as follow: -Time period selection: this part could be changed to select a study time period by choosing an initial and final date. -Data selection and transformation: the data corresponding to this time period are selected in 345 the different files. Electric signals measured by the water level sensors are converted in water level (by using Eq. 4 and 10), then in discharge by using Manning-Strickler equation (Eq. 5) for the pipe and Eq. 11 for the storage unit. In order to smooth the erratic 15s-signal produced by storage unit measurements, the computed discharge data are averaged on a moving window, whose number of time steps can be modified. Dielectric constants measured by the 350 16 TDR sensors are converted in water content by using Topp equation (Eq.
: https://doi. org/10. 5281/zenodo. 3467300 It is provided by the Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech 410 (HM&Co-ENPC). The following references should be cited for every use of the data: Versini, P. -A. , Stanic, F. , Gires, A. , Scherzer, D. , and Tchiguirinskaia, I. (2019). Measurement of the water balance components of a large green roof in Greater Paris Area. Earth System Science Data. XXXXX Versini, P. -A. , Stanic, F. , Gires, A. , Schertzer, D. , Tchinguirinskaia, I. : Data for "Measurement of the 415 water balance components of a large green roof in Greater Paris Area", https://doi. org/10. 5281/zenodo. 3467300, 2019.
Acknowledgment This work was initiated during the  Climate-KIC  funded  Blue Green Dream  project (http://bgd. org. uk/). It has also been supported by the  Academic Chair "Hydrology for Resilient Cities ", a partnership between  Ecole des Ponts ParisTech  and the  Veolia group , and the  ANR EVNATURB project dealing 440  with the evaluation of ecosystem performances for re-naturing urban environment.
Biodiversity loss is a major challenge. Over the past century, the average rate of vertebrate extinction has been about 100-fold higher than the estimated background rate and population declines continue to increase globally. Birth and death rates determine the pace of population increase or decline, thus driving the expansion or extinction of a species. Design of species conservation policies hence depends on demographic data (e. g. , for extinction risk assessments or estimation of harvesting quotas). However, an overview of the accessible data, even for better known taxa, is lacking. Here, we present the Demographic Species Knowledge Index, which classifies the available information for 32,144 (97%) of extant described mammals, birds, reptiles, and amphibians. We show that only 1. 3% of the tetrapod species have comprehensive information on birth and death rates.
We found no demographic measures, not even crude ones such as maximum life span or typical litter/clutch size, for 65% of threatened tetrapods. More field studies are needed; however, some progress can be made by digitalizing existing knowledge, by imputing data from related species with similar life histories, and by using information from captive populations. We show that data from zoos and aquariums in the Species360 network can significantly improve knowledge for an almost eightfold gain. Assessing the landscape of limited demographic knowledge is essential to prioritize ways to fill data gaps. Such information is urgently needed to implement management strategies to conserve at-risk taxa and to discover new unifying concepts and evolutionary relationships across thousands of tetrapod species.