dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Benzodiazepine characteristics included the generic name, prescription dates and delivery dates. All benzodiazepine deliveries (i. e. , instances of dispensing medication) recorded in the database were extracted from April 1 to June 30, 2011, and from April 1 to June 30, 2012, and the corresponding patients were identified. Other benzodiazepine deliveries were tracked for a longer period for each patient, from December 1, 2010, to August 20, 2011, and from December 1, 2011, to August 20, 2012. The proportion of patients older than 65 years who received a long half-life benzodiazepine was calculated. Only the first benzodiazepine was considered in the analysis when a patient had been successively prescribed two different benzodiazepines. The data collected included patient characteristics, such as gender, age, socioeconomic status (characterised by specific reimbursement facilities) and two types of medical history information: diagnosis of a chronic disease (including patients benefiting from "disorder of long duration" reimbursement status) and whether a GP or a psychiatrist initiated the prescription.
Main findings The proportion of patients who continued their benzodiazepine prescriptions beyond the recommended duration did not decrease between 2011 and 2012, despite recommendations and financial incentives. On the contrary, this database study shows a slight but significant increase in the number of patients who did not interrupt benzodiazepine consumption. One in five patients who initiated a benzodiazepine regimen continued drug intake beyond the recommended 12-week duration, which increased to more than one in four patients over the age of 65. The proportion of long half-life benzodiazepine prescriptions decreased in the latter population, which could be attributed to a 20% increase from 2011 to 2012 in the overall prescription of short half-life benzodiazepines, compared with no change in long half-life benzodiazepine prescription.
An optional pay-for-performance system had been piloted in France previously; consequently, the effective novelty of the pay-for-performance scheme probably concerned only two-thirds of the GPs who participated to the study. Information in this study was extracted from large databases derived from healthcare insurance systems, which is similar to previous studies  [24] [25] [26]. A limitation reported in other studies of inappropriate prescribing was that information about disease and indications could not be considered. Drug intake could be assessed using only proxy measures because data collection was based on reimbursement.
This dataset provides information on specimens of harvestmen (Arthropoda, Arachnida, Opiliones) collected in French Guiana. Field collections have been initiated in 2012 within the framework of the CEnter for the Study of Biodiversity in Amazonia (CEBA: www. labexceba. fr/en/). This dataset is a work in progress. Occurrences are recorded in an online database stored at the EDB laboratory after each collecting trip and the dataset is updated on a monthly basis. Voucher specimens and associated DNA are also stored at the EDB laboratory until deposition in natural history Museums.
The maximum richness of harvestmen species is found in the Neotropical region, however, diversity patterns within this region are still poorly known, both on regional and local scales. Because of their relatively high abundance and diversity in the Neotropical forests, they are useful candidates for studying community ecology (e. g. Proud et al. 2012) , phylogeography, and biogeography (e. g. Sharma and Giribet 2012). By providing a database on harvestmen of French Guiana, regularly updated and publicly available, our goal is to provide a fast and efficient tool for sharing and tracking information on collected specimens. This database will be used for disseminating biodiversity information related to ongoing work on ecology and evolution of harvestmen in French Guiana. We also aim to promote best practices for recording and sharing biodiversity data within our research community.
Design description: This dataset was developed to determine the current distribution patterns of harvestmen species at the scale of French Guiana. Thus locations to be sampled were selected to maximize the geographical coverage. For some collecting trips (e. g. Saül 2013), a specific design was implemented to sample over three topographic units: hilltop, slope and seasonally-flooded bottomland. Funding: Data for this resource have been obtained within the framework of grants DIADEMA and PHYLOGUIANAS from the Labex CEBA (Laboratoire d'Excellence Center for the Study of Biodiversity in Amazonia). CEBA is funded by "Investissement d'Avenir" grant managed by the French National Research Agency (ANR) under grant number ANR-10-LABX-25-CEBA.
Validity of the taxonomic names used was checked against the Encyclopedia of Life (http://www. eol. org) and the GBIF backbone taxonomy (http://www. gbif. org/dataset/ d7dddbf4-2cf0-4f39-9b2a-bb099caae36c). Future validity checks could use the "World Checklist of Opiliones species", once it is published in its entirety  (Kury et al. 2014). Step description: After collection, samples are sorted and placed in individual tubes containing 95% ethanol with a unique identifier until further processing in the laboratory. Codes are based on locality, collection date, collection method followed by a unique 2 2 2 number as in GF140308HC001-07 which indicates that the specimen was collected in French Guiana (GF) on 2014-03-08 (yyyy-mm-dd), and hand collected (HC). Collection method codes include sweep net (SN, SND or SNN), beating (BND or BNN), pitfall (PT), winkler (WK, WKD or WKN).
When specimen collection was part of a larger field expedition, we kept the original codes that might not follow this rule (as in SL13-1208-OP01 collected in Saül). Collecting information for each specimen is databased in an online Voseq database (Peña and Malm 2012) hosted by the EDB laboratory (Fig. 1 ). Specimens are photographed using an Olympus DP20 mounted on a Leica MZ16 binocular and pictures are stored in the database. For each specimen, 1 or 2 legs are kept in a separate vial and stored at -20°C for further molecular investigation. Specimens are initially curated at the EDB laboratory by J. Murienne and can be deposited in museums for further taxonomic study. For example, specimens of Sclerosomatidae have been sent as gift to the Museum of Comparative Zoology (MCZ) at Harvard University, Cambridge USA, to be investigated by Ana Lucia Tourinho.
The current localisation of the specimens (either at EDB or in museums) can be found under the "disposition" field. Example data entry of our online database (http://edbase. ups-tlse. fr with restricted access) holding the "Harvestmen of French Guiana" dataset. The database can be downloaded as a spreadsheet at: http://130. 120. 204. 55:8080/ipt/resource. do?r=harvestmen_of_french_guiana or at: http://www. gbif. org/dataset/3c9e2297-bf20-4827-928e-7c7eefd9432c.
Taxonomic coverage Description: This database is devoted to all harvestmen species inhabiting French Guiana. Little is known about the taxonomy of the group in French Guiana but some information are available in  Kury (2003)  for Laniatores and Benavides and Giribet (2013) for Cyphophthalmi. Identifications to the family level were conducted based on Kury and Pinto-da-Rocha (2002) and Pinto-da-Rocha et al. (2007) (Fig. 3 ). Three families are here reported for the first time in French Guiana, namely Agoristenidae, Fissiphalliidae and Icaleptidae. Identifications of Stygnidae to the species level were conducted based on Pinto-da-Rocha (1997).
Column label Column description occurrenceID An identifier for the Occurrence (as opposed to a particular digital record of the occurrence). In the absence of a persistent global unique identifier, construct one from a combination of identifiers in the record that will most closely make the occurrenceID globally unique. See also http://rs. tdwg. org/dwc/terms/ index. htm#occurrenceID For line numbers you can specify an optional non-numerical suffix to be appended to the id. This is useful to generate unique identifiers when mapping the same source multiple times. institutionCode The name (or acronym) in use by the institution having custody of the object(s) or information referred to in the record. collectionCode The name, acronym, coden, or initialism identifying the collection or data set from which the record was derived.
Acknowledgements The authors would like to thank the organizers of logistical support for the field missions ( C. Baraloto ,  A. Fouquet ,  Parc Amazonien de Guyane, CNRS Guyane ), all the contributors that helped collecting samples in the field during the CEBA missions ( V. Vedel ,  A. Cerdan ,  J. Orivel  and  J. Donald ) as well as those who kindly provided us specimens ( A. Fouquet ,  C. Duplais ,  J. -P. Vacher ,  P. O. Cochard ,  M. Berroneau ,  L. Barthe ,  J. Pétillon  and  A. Lorenzini ) and helped in species identifications ( G. Giribet ,  A. Kury ,  A. Perez ,  R. Pinto-da-Rocha ,  A. L. Tourinho ). We would also like to thank the  GBIF France  ( M. -E. Lecoq  and  S. Pamerlon ) for helping us with setting up the IPT. The EDB lab is supported by " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ;  TULIP :  ANR-10-LABX-41 ,  ANR-11-IDEX-0002-02 ).
Daily interactions naturally define social circles. Individuals tend to be friends with the people they spend time with and they choose to spend time with their friends, inextricably entangling physical location and social relationships. As a result, it is possible to predict not only someone's location from their friends' locations but also friendship from spatial and temporal co-occurrence. While several models have been developed to separately describe mobility and the evolution of social networks, there is a lack of studies coupling social interactions and mobility. In this work, we introduce a model that bridges this gap by explicitly considering the feedback of mobility on the formation of social ties. Data coming from three online social networks (Twitter, Gowalla and Brightkite) is used for validation.
I. INTRODUCTION The advent of the big data revolution has opened the door to the analysis of massive datasets on all aspects of society. New technologies have made possible the access to unprecedented amount of information on human behavior generated unobtrusively whenever people interact with or through modern technologies such as cell phones, online services, mobile applications, etc. This fact is facilitating the pursuit of a computational approach to the study of problems traditionally associated with social sciences  [1]. Over the course of the last few years, it has allowed for the development of greater insights, for instance, into human mobility  [2] [3] [4] , structure of online social networks  [5, 6] , cognitive limitations  [7, 8] , information diffusion and social contagion  [9] [10] [11] [12] [13] , the importance of social groups  [13] [14] [15]  or even how political movements raise and develop  [16] [17] [18].
Multi-parametric inference methods have been applied to empirical data with the aim of predicting link presence and users' locations  [30] [31] [32]. These works show that the accuracy of link prediction is considerably improved by taking into account the geographical information, and that the accuracy of location prediction is enhanced when the online social links are provided. The availability of geo-localized information has also allowed for a detailed exploration of human mobility  [2, 3, 21, [33] [34] [35] [36]. The length of displacements between locations was found to follow a broad distribution, well fitted by a power-law decaying function  [2, 3]. The asymmetry of the travels was studied by considering ellipsoidal boundaries to the average individual displacements and analyzing the scaling of the radius of gyration.
II. THE DATASETS We have collected data from online social networks containing both social links and information about the users' physical positions. The first dataset was obtained from Twitter by means of its API  [42]. We identify over 714, 000 single users, who tweeted using a GPS enabled mobile device during the month of August 2011  [43]. If those users reported various locations in different tweets, the most recent one is taken for the purpose of the study. The other two datasets contain information referring to the users' location check-ins and the social networks of Gowalla and Brightkite  [31]. Both were location-based online social networks, in which users can check-in at their current locations and receive information about services in the area as well as about their friends' positions.
Gowalla and Brightkite are no longer active but their data is available online  [44]. The main statistical features of our three datasets are displayed in Table  I. Social interactions across country borders have particular properties and are affected by political, linguistic or cultural factors. We overcome this difficulty by restricting our analysis to the networks within each country. Intra-country mobility and social contacts account for the large majority of a user activity  [45, 46]. For simplicity, we focus on the three major countries with more than one thousand users in each of our datasets: the United States (US), the United Kingdom (UK) and Germany (DE). We have analyzed and modeled other countries and found similar results to the ones presented in this manuscript. TOTAL(×10 3 ) US(×10 3 ) UK(×10 3 ) DE(×10 3 ) N L N L N L N L.
IV. GEO-SOCIAL PROPERTIES OF THE NETWORKS We start by establishing a set of metrics in order to characterize networks structure and its relation to geography. First, we measure the probability of two users to have a link at a certain distance P l (d). It is defined as the ratio between the number of existing links at distance d and the total number of users pairs separated by d, and thus it is constrained to the interval [0, 1]. P l (d) decays slowly with the distance for empirical networks, essentially as a power-law with exponent -0. 7, which is followed by a plateau for very large distances (see Figure  2A ). This functional shape remains identical for all the countries and all the datasets considered. It matches, besides, the behavior reported in the literature for online social systems  [22, 26].
A second metric that we consider is the degree distribution of the social networks (see Figure  2B  for the empirical networks). For Twitter, which has a directed social network, we consider the degrees of its symmetrized version. The distribution P (k) displays heavy tail in all the datasets, even though there are slight differences between them. Connections in Twitter are directed: one user follows the messages emitted by another. Reciprocated connections indicate mutual interest between the two users and a closer type of social relation  [8, 14]. To assess how geography and reciprocity correlate, we measure the probability R (d) of reciprocation conditional on a link at a distance d (Figure  2C ). We find that the reciprocity decreases with the distance in all the countries analyzed. This trend is consistent with the idea that stronger relations occur close to where users spend most of their time, with some longer connections composed of friends who moved, former residences, online acquaintances, etc.
Furthermore, long not-reciprocated connections may include users following public figures or celebrities. With the aim of quantifying social closeness between users, we define the social overlap J f of two connected users i and j as J f = |K i ∩ K j | |K i ∪ K j | -2 (1) where K i represents the set of friends of user i. J f is inspired by the Jaccard index but is modified to ensure that it takes a value of 1 if i and j share all their friends, and 0 if they have no common friends. In Figure  2D , the average of the social overlap J f (d) over all pairs of connected users is plotted as a function of the distance between them. The social overlap decreases with the distance. The functional shape of the curves is similar for all the datasets, even though the overlap level is different for each of them.
The effect of the distance on the clustering coefficient can be incorporated by measuring the distances from each central node j to two neighbors i and k forming a triad, d = d ij + d jk , and calculating the network clustering restricted to triads with distance d. This new function C(d) is the probability of closing a triangle given the distance d in a triad C(d) = ∆(d) Λ(d) , (2) where Λ(d) and ∆(d) are the numbers of triads and closed triads for the distance d, respectively. The value of the global clustering coefficient C can be recovered by averaging C(d) over d. In the datasets, we observe a drop in C(d) followed by a plateau, which is best visible for the US networks (Figure  2E ). Given a triangle, several configurations are possible if there is diversity in the edge lengths. The triangle can be equilateral if all the edges have the same length, isosceles if two have the same length and the other is smaller, etc.
• R(d): The probability of reciprocation conditional on a link at a distance (Figure  2C ). • J f (d): Average overlap as a function of the distance (Figure  2D ). • C(d): Clustering coefficient as a function of the triad distance (Figure  2E ). • P (D): Distribution of distance disparity for the triangles' edges (Figure  2F ). We will use these metrics in the coming sections to estimate the ability of model to produce social networks comparable with those obtained from the empirical datasets. V. RESULTS.
The parameter space is covered uniformly in a linear scale for p v and in a logarithmic one for p c. For all the countries, the minimum value of the error is obtained for p v in the interval (0. 05, 0. 3) and p c in the range (5 • 10 -3 , 5 • 10 -2 ). The values of Err found at the minimum are 0. 30 for the US, 0. 18 for the UK and 0. 39 for Germany. For simplicity, we focus on the Twitter networks only, although similar results are obtained for the other datasets.
Random link creation accounts for online acquaintances or for historical face-to-face encounters as individuals move their residence from one city to another. Finally, individual random mobility allows the agents to explore new locations. 4 ur results show that by establishing an appropriate balance between friend visits and random link creation, the model can reproduce the main features of online social networks, e. g. , we show that 10% -30% of the mobility has to be directed towards existing friends. We demonstrate that these are the fundamental mechanisms at play in the model. The TF model is generic and functional for different datasets. Human mobility driven by social ties has impact on the modeling of disease spreading, and may improve its predictions. This model can also be used in simulations of processes that involve social networks and geography, e.
historical data The historical overnight rate data 2 (4 January 1999 to 5 June 2013) were used as follows. The long-time period data-set (4 January 1999 to 11 July 2012; Long Period a) covering 3464 time points, the short-time period data-set (11 July 2011 to 11 July 2012; short Period B) corresponding to 259 time points, and the medium-time period data-set (4 January 1999 to 31 December 2004; Medium Period c; 1534 time points) were chosen for the calibration of the model. The out-of-sample simulations of overnight rates were tested for two different time periods: from 1 January 2005 to 30 December 2011 (using calibration from the period c) and from 12 July 2012 to 5 June 2013 (using for comparison the two cases of calibration-the period a and the period B).
The RTMs used and their inputs The present study is a pure modeling assessment. It is performed with the RTM libRadtran version 1. 7  (Mayer and Kylling, 2005; Mayer et al. , 2012). In addition, the RTM SMARTS version 2. 9. 5 (Simple Model of the Atmospheric Radiative Transfer of Sunshine, Gueymard, 1995) is used to check that conclusions do not depend on this RTM and the aerosol model. SMARTS is limited to clear sky conditions. These two RTMs provide spectral irradiances and have been chosen because several articles have demonstrated the quality of their results when compared to spectral measurements. The libRadtran (library for Radiative transfer) package software is a convenient set of tools for calculating radiative transfer in the Earth atmosphere. The main tool uvspec includes both the correlated-k approximation of  Kato et al.
(1999)  and the pseudo-spectral calculations of the RTM SBDART. The correlated-k approximation uses the HITRAN molecular spectroscopic database. The pseudo-spectral calculations use the parameterization of absorption cross sections from LOW-TRAN. The DISORT 2. 0 (discrete ordinate technique) algorithm  (Stamnes et al. , 2000)  was selected to solve the radiative transfer equation in both cases. Here, lib-Radtran is used twice: one in order to produce the spectral SSI in each Kato band, the second to produce detailed spectral calculations in each band serving as reference for the comparison. The inputs to libRadtran governing the optical state of the clear atmosphere are the total column contents in ozone and water vapour, the vertical profile of temperature, pressure, density, and volume mixing ratio for gases as a function of altitude, the aerosol optical depth at 550 nm, Angström coefficient, and aerosol type, and the elevation of the ground above sea level.
Acknowledgements The authors thank the teams developing libRadtran (http://www. libradtran. org)and SMARTS. They also thank  S. Kato  for encouraging them to undertake the study. This work was partly funded by the  French Agency ADEME in charge of energy  (grant no. 1105C0028 ,  2011-2016 ) and took place within the  Task 46  "solar resource assessment and forecasting" of the Solar Heating and Cooling programme of the  International Energy Agency.
INTRODUCTION: Up to now, operational oceanography systems (OOS) which provide decision makers with oil spill trajectory forecasting, have demonstrated their usefulness in dealing with recent crisis involving dramatic environmental and socio economic impacts  (Castanedo et al. , 2006). Accordingly, numerous national and regional OOS have been set up all over the world in order to facilitate and support an adequate response and preparedness (e. g. Olabarrieta et al. , 2007). However, occasionally, the available spatial data resolution of these systems is insufficient to be used for coastal applications, and therefore, this information must be transferred to shallow water, in order to increase the spatial resolution (namely downscaling).
, 2007) ; (4) a web service that manages the operational system and allows the user to run hypothetical as well as real oil spill trajectories using the daily forecast of wind and high resolution (O(20 m)) ocean variables carried out by COAWST. Figure  2  shows a scheme of the structure of the system. As can be observed, the HR-OOS is composed of two main modules: the HR operational oceanographic module (HR-OOM) and the HR oil spill forecast module (HR-OSM). The HR-OOM is based on COAWST modelling system (A Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modelling System)  (Warner et al. , 2010). COAWST is comprised of the Model Coupling Toolkit to exchange data fields between the ocean model ROMS, the atmosphere model WRF, the wave model SWAN, and the sediment capabilities of the Community Sediment Transport Model.