dataesr/scientific-paragraphs-categorization huggingface dataset:
The use of traits can therefore greatly improve our understanding of ecological patterns and processes and their relevance for the conservation of biodiversity. Trait data are usually published in widely dispersed literature and therefore difficult to access. Hence, a compilation of such data in handbooks (e. g. Novosolov et al. 2013)  or in databases is essential to support the study of large-scale ecological processes and patterns. Due to their key role in ecosystem processes, traits for several groups of species have been compiled and made available, e. g. several plant traits  [Klotz et al. 2003 (www. biolflor. de) , Kleyer et al. 2008 (www. leda-traitdatabase. org), Kattge et al. 2011 (www. try-db. org) ], bees  [Bees, Wasps & Ants Recording Society 2014 (www. bwars. com) ], syrphids  [Speight et al.
2000 (www. iol However, we are not aware of a global species trait database that exists for reptiles although a general compilation of reptile species names and distribution exists  (Uetz and Hošek 2014)  and a compilation of trait data have been published in printed form e. g. for Mexico  (Sinervo et al. 2010 ) and some selected traits of 641 lizard species from around the world  (Novosolov et al. 2013). For this reason, within the project SCALES  (Henle et al. 2010)  we developed a trait database covering all European reptile species since the project was predominantly based in Europe. We mainly aimed to obtain phenological and movement data from across the geographic distribution of the species covered. However, other information on activity, energy, habitat, sexual maturity, and morphometry were also included.
The reptile trait database is an open access database. A user-friendly interface including browse options is provided on the SCALES project webpage (http://scales. ckff. si/scaletool/ ), but we also provide access to raw data through Dryad (doi: 10. 5061/ dryad. hb4ht). We will keep the database active and update it frequently. We therefore invite all of our readers to provide published data that can be added to the database. If you are interested in submitting data, please contact the authors and send the respective papers. The data will be checked by us for plausibility (especially non-peerreviewed publications) before being entered into the database.
Taxonomic coverage General taxonomic coverage description: The coverage of this database spans the class of Reptilia in the Kingdom Animalia. The database collates the species traits of all 122 European species belonging to 43 genera recognized by the SEH Atlas of 2004  (Gasc et al. 2004) , which was the most up-to-date list when we started our data compilation (referred to as SEH taxonomy in the database; Suppl. material 1: Table  S1 ). Since the atlas was published, many names changed both at and below the generic level, primarily by splitting previous taxa. Currently, there are several lists of European reptile species available  (Sindaco and Jeremčenko 2008 , Cox and Temple 2009 , Speybroeck et al. 2010 , Mayer 2013 , Glandt 2014 , Sillero et al. 2014 , Uetz and Hošek 2014 ) that deviate from each other in recognition of some taxa and also in terms of geographic coverage (see below).
For inclusion in the database, an additional criterion had to be fulfilled: the distribution of the taxa involved must have been worked out sufficiently to allow allocation of life-history data to a particular taxon without uncertainty. This resulted in 144 recognized species belonging to 59 genera (named current taxonomy in the database; Suppl. material 1: Table  S1 ). Deviations from  Speybroeck et al. (2010)  and  Sillero et al. (2014)  are listed and justified in Suppl. material 2: Table  S2. The database is designed in such a way that taxa (European or non-European) can be added without a need to change the structure of the database. The database can be searched using both the original names as in  Gasc et al. (2004)  as well as the updated names. We allocated data that were published before species complexes were split to the relevant new taxa if this allocation could be made with certainty.
Taxonomic ranks Kingdom: Animalia. Phylum: Chordata. Class: Reptilia. Order: Testudines, Squamata (Amphisbaenia, Sauria, Serpentes). Family: Agamidae, Anguidae, Blanidae, Boidae, Chamaeleonidae, Cheloniidae, Colubridae, Dermochelyidae, Emydidae, Gekkonidae, Geomyidae, Lacertidae, Lamprophiidae, Natricidae, Phyllodactylidae, Scincidae, Sphaerodactylidae, Testudinidae, Typhlopidae, Viperidae. Family names of reptiles also changed since 2004. Our list follows the family names accepted in the TIGR global reptile database  (Uetz and Hošek 2014). Common Name: Reptiles, Snakes, Lizards, Tortoises.
Spatial coverage General spatial coverage: Our database covers Europe. Neither politically nor geographically has the boundary of Europe been universally agreed upon and overviews of the distribution of reptile species used different existing delimitations  (Mertens and Wermuth 1960 , Böhme 1981 , Gasc et al. 2004 , Cox and Temple 2009 , Speybroeck et al. 2010 , Sillero et al. 2014). We followed  Mertens and Wermuth (1960,) Böhme (1981) , and  Gasc et al. (2004) , using the following delimitation of "Europe": reaching across the Ural Mountains, the Ural River, the Caspian Sea, the main Caucasus Divide, the Black Sea, including the Marmara Sea and the Aegean Sea along the divide of the European and Asiatic shelf as well as the European and African tectonic plates (Figure  1 ). Mascarene Island and the Azores, which politically but not geographically belong to Europe are currently not included, nor are the Selvagenes and the Canary Islands.
Our data therefore covers 46 European countries. Speybroeck et al. (2010) , in contrast, did not include former republics of the Soviet Union that are partially or completely within Europe (see Suppl. material 2: Table  S2. 1). Cox and Temple (2009)  Figure  1. Spatial coverage of the database. Grey countries (46 European countries plus neighbouring countries in Asia, namely Armenia, Azerbaijan, Georgia, Iran, Israel, Kazakhstan, Kyrgyzstan, Lebanon, Russia, Syria, Tajikistan, Turkey, Turkmenistan, and Uzbekistan, plus the African countries Algeria and Morocco) are covered by the database. This map shows the countries in general and not the region where data points are from. This map was created using ArcGIS® software by Esri (www. esri. com) with an European Lambert Azimuthal Equal Area projection.
© Esri, all rights reserved. are inconsistent in including Greek islands on the Asian shelf but excluding Mediterranean Spanish islands on the African shelf. They further included the Mascarene Island, Selvagenes, and the Canary Islands. Our database is designed in such a way that countries or other geographic entities can be added without a need to change the database structure. As we wanted to cover European species comprehensively, we also included extra-limital data in the database. We found data for neighbouring countries in Asia (Armenia, Azerbaijan, Georgia, Iran, Israel, Kazakhstan, Kyrgyzstan, Lebanon, Russia, Syria, Tajikistan, Turkey, Turkmenistan, and Uzbekistan) and Africa (Algeria, Morocco) (Figure  1 ). In addition, case studies from the USA about the invasive European gecko Hemidactylus turcicus are included as such data are highly valuable for an assessment of invasion processes of European species.
The designation of geographical entities in the database does not imply the expression of any opinion whatsoever on the part of the data compilers concerning the legal status of any country, territory or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. It is worth noting that in some older references, older geographic names are used that are difficult to match with modern names; in such cases we either retained the old names or used a more inclusive geographic name that encompasses the name in question. The database enables all geographic names to be listed that are used in the database.
Method step description: We carried out a literature survey in the form of focal species surveys of all European reptiles. A very useful starting point was the Handbook of European Reptiles (in German)  (Böhme 1981) , as well as French (Arnold and Ovenden 2010) and Spanish  (Escarré and Verricard 1981, Salvador and Marco 2009)  handbooks and our own extensive collection of life-history publications. These sources already compiled a substantial part of the relevant publications. For species, for which we retrieved no data in above sources, we conducted targeted searches in the ISI web of knowledge, in Google Scholar, and in Google published in English, German, French, and Spanish. We tried to trace any potentially relevant sources cited in the publications found from these searches. Moreover, we presented our project at herpetological conferences to expand our literature sources from experts.
However, we only entered data from published literature into the database. The publications that were used to provide data for the database are listed in References. Together, these sources covered all European reptile species except a few recently described taxa, such as Dalmatolacerta montenegrina, for which no life-history data have been published. Study extent description: All European reptile species are covered in the database without accounting for temporal restrictions as to when the study was conducted. Sampling description: Before starting the literature search, we decided upon a database structure (Figure  2 , described below) and the main topics to be covered. The main topics selected were activity and energy traits, phenological traits, movement/ dispersal traits, age at sexual maturity, and morphometry.
Dataset descriptions Our database comprises two sections: basic information and actual trait data. In the basic section, general information about taxonomy, references, and countries is stored. The trait data section is divided into the following: Activity / Energy / Habitat; Phenology; Sexual Maturity; Movement; and Morphometry. Zero Waiver: http://creativecommons. org/publicdomain/zero/1. 0/. Thus, the data can be freely used for non-commercial purposes provided the source is acknowledged. See Creative Commons for more details of the conditions of usage.
Metadata descriptions Our relational database is divided into a basic section and the actual trait section (Figure  2 ). The basic section comprises three tables: Species, which lists all 144 European reptile species that we recognised (see the section on taxonomic coverage); References, listing all published sources for data extraction (166 in total); Countries, listing 46 European, 14 Asian, and 2 African countries and the USA, which is where reptile trait data stem from including their ISO 2, ISO 3, and ISO No codes. In addition, we have names for supranational geographic regions, such as the Caucasus or the Mediterranean, for data where it is not clear from the original source to which country they apply. In the trait data section, five main tables were created according to the five main topics (Activity / Energy / Habitat; Phenology; Movement; Sexual Maturity; Morphometry).
Movement: Data on movement cover true dispersal, home range movement, and migration because most references do not sufficiently differentiate between these processes. However, if data were sufficiently explicit, we solely used dispersal data. Movement data larger than 250 m were rounded off to 250 m. The reference time span is not necessarily one year but depends on the study described in the original paper. The data in the database are the highest values given by the reference publication for a specific reference area. If a range of maximum dispersal/movement was given in the reference, we provide the lower value in the column maximum movement low and the higher value as maximum movement high. If no range was given, data were allocated to the latter. Furthermore, passive dispersal provides information about whether a species may be dispersed passively through human activities.
These traits always refer to denoted parts of the population (Who), such as females, males, hatchlings, juveniles, eggs, or clutches. This structuring allows a maximum number of possible combinations, while using minimal memory space. Since our database was built as a relational database, IDs between the basic and the trait tables were linked to each other for fast searches and queries (Figure  2 ). Metadata language: English Date of metadata creation: 2014-09-12 Hierarchy level: Database.
Data have been made available under the Creative Commons CC- Object name: Trait database of reptile life histories Character encoding: UTF-16 Format name: Microsoft Access Database Format version: Microsoft® Access® 2010 (14. 0. 7104. 5000) SP2 MSO (14. 0. 7116. 5000) as part of Microsoft Office Professional Plus 2010 Distribution: http://scales. ckff. si/scaletool/?menu=6&submenu=0 and http://dx. doi. org/10. 5061/dryad. hb4ht Publication date: 2014-01-21 and 2014-12-08, respectively Language: English Licenses of use:.
The complex nature of zoonotic diseases and the limited resources in developing countries are a reminder that the need for implementation of Global One Health in lowresource settings is crucial. The Veterinary Public Health and Biotechnology (VPH-Biotec) Global Consortium launched the International Congress on Pathogens at the Human-Animal Interface (ICOPHAI) in order to address important challenges and needs for capacity building. The inaugural ICOPHAI (Addis Ababa, Ethiopia, 2011) and the second congress (Porto de Galinhas, Brazil, 2013) were unique opportunities to share and discuss issues related to zoonotic infectious diseases worldwide. In addition to strong scientific reports in eight thematic areas that necessitate One Health implementation, the congress identified four key capacity-building needs: (1) development of adequate science-based risk management policies, (2) skilled-personnel capacity building, (3) accredited veterinary and public health diagnostic laboratories with a shared database, and (4) improved use of existing natural resources and implementation.
Better reporting systems from medical care facilities and a comprehensive national database that includes reports from environmental monitoring and human and animal health diagnostic systems are key components of an integrated surveillance system. This is deemed essential for One Health implementation. Collaborative regional networks that promote standardized curriculum for graduate studies within the framework of the One Health approach are currently One example is the SAPU-VETNET, a partnership between Latin America and the European Union. Needs for capacity building for scientists, extension officers, physicians, veterinarians, and technicians and better extension services at the farm or community level were also pointed out as urgent topics to improve rural economic development and capacity building.
The congress delegates recognized that operationalizing One Health will enable collaborators to more effectively address and reduce the burden of zoonotic infectious diseases worldwide. To that end, the congress identified four areas for capacitybuilding needs, mainly directed at low-resource settings: (1) the development of adequate science-based risk management policies that respect transboundary and international guidelines, (2) sustained capacity building of applicably and appropriately knowledgeable and skilled One Health personnel, (3) accredited environmental and clinical diagnostic laboratories with an integrated and shared database, and (4) ensuring improved use of existing natural resources and implementation plans based on cost-benefit analyses. The findings obtained in the two ICOPHAI congresses encourage us to further promote One Health, and the forum will resume with its next congress in Chiang Mai, Thailand, in August 2015.
© 2014 Abdesselam et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
We calculated eGFR using the modified Schwartz formula indexed to an international creatinine standard. 3 We measured patient height by a stadiometer calibrated to the nearest 0. 1 cm, and body mass by a digital scale calibrated to the nearest 0. 1 kg. We calculated body surface area by a standard equation. We calculated the height Z-score for age using the Centers of Diseases Control and Prevention growth charts. 4 We measured quality of life by using the PedsQL. All data are reported for 24 months of treatment with DR-CYS. In general, cystine is quantitated from a human white blood cell lysate prepared at each local study center by mixing the clear supernatant obtained after centrifugation with isotopelabeled cystine as an internal standard, and quantitated by liquid chromatography mass spectrometry/mass spectrometry.
Statistical Analyses Longitudinal data were analyzed using a mixed model ANOVA. This model allowed for a variable number of repeat evaluations per subject and took into account the variability specific to each subject (intrasubject variance). Linear regressions were then calculated using the least-square means for each time point. Statistical significance is P <. 05. Statistical analyses were performed with SAS Software v 9. 2 (SAS Institute, Cary, North Carolina).
Acknowledgments Supported by  Raptor Pharmaceuticals Inc and National Center for Advancing Translational Sciences  of the  National Institutes of Health  ( UL1TR000150 ,  UL1TR000454 , and  UL1 TR000093 ). Raptor Pharmaceuticals also supported the patients' travel during the study. M. B. and P. R. are employees of Raptor Pharmaceuticals Inc. We thank the patients, their parents, and the study personnel at each site for their sustained interest, participation, and expertise in carrying out this study.
MATERIALS AND METHODS We gathered data from 28 independent studies across 14 countries, a study being defined as a dataset collected independently in a given habitat and locality (Table  S1  in Supporting Information). Criteria used for the inclusion of a particular study are described in Appendix S1. Twenty studies were conducted in Europe, four in Africa, one in North America, one in South America and one in Australia (Fig. 1a ). These studies covered a range of (semi) natural and managed habitats. Each study collected data on flower visitation of plant communities in multiple sites or time periods, leading to a total of 750 plant-pollinator quantitative networks involving 1524 plant species and 3100 pollinator species or morphospecies. Data for each network were gathered in a relatively small study area and during a restricted time period (see study details in Table  S1  and Appendix S1) to minimise the number of impossible interactions due to spatial and temporal mismatch, i.
Data analyses Although M€ uller's index was calculated taking into account all plants in each network, not all plant pairs were included in subsequent analyses. First, we only included pairs for which we had information on flower abundance, floral colour, height, display, flower shape, nectar tube length and nectar splitting within a floral unit (see traits methods description below) and phylogeny for both acting and target plant. Second, all pairs in which the target plant did not receive any visits were excluded, as we could not assess the contribution of other plants to the diet of its pollinators. These selection criteria resulted in a total of 98108 records of plant pairs, corresponding to 564 plant species (see Table  S1 ). The four hypotheses presented in the introduction were tested with Generalised Linear Mixed Models using package lme4  (Bates et al.
To analyse the probability of a plant influencing another with the Binomial model, we used all 98108 records of plant pairs. To analyse variability of M€ uller's Index among species that shared pollinators with the Gaussian model, we used 12587 records of plant pairs. However, nectar sugar content, a variable required to test our second and third hypotheses, was only available for a limited number of European and African plant species (164 of the total 564 plant species included in pairs selected for data analyses, see Table  S1 ). Consequently, we repeated the analyses with the subset of plant pairs (hereafter 'reduced dataset') for which we had nectar sugar content data, considering the results for all four hypotheses. The reduced dataset consisted of 49694 records of pairs for the analyses of probability of sharing visitors (Binomial model) and to 6765 records of plant pairs when analysing the variability of M€ uller's index among species that shared pollinators (Gaussian model).
RESULTS Resource availability and accessibility, as well as phylogenetic distance between plants, explained a significant part of the variability in the probability of one plant species sharing pollinators with another plant (Binomial models results in Table  1  for the full dataset, and in Table  S3  for the reduced dataset which accounts for the effect of nectar sugar content). These variables also explained the variability in the influence strength of one plant on other plants in the community (Gaussian models results presented in Table  2  for the full dataset, and in Table  S4  for the reduced dataset). The most parsimonious Gaussian model explained of the total deviance of the data (model 1 of Table  2 ). Flower resource availability terms explained most of such deviance, followed by accessibility terms and phylogeny.
Effect of flower resource availability (hypothesis 2) As expected, increasing flower abundance of a plant species (relative to the whole plant community) increased its probability of influencing other plant species (binomial model; Table  1 ), as well as the average strength of such influence (Gaussian model, Table  2 ). This positive relationship was nonlinear (logit-log e, Fig S7 ), with a decelerating slope indicating that above a certain flower abundance threshold all plants have a similar chance of influencing co-flowering species. The effect of acting plant relative abundance was also dependent on the abundance of the target plant (i. e. target plant flower abundance relative to the acting plant had a significant effect, Fig. S6 ). When repeating the analyses with the reduced dataset, the positive influence of flower abundance was maintained (Table  S3 , S4).
Effect of nectar accessibility (hypothesis 3) Nectar tube length and shape were positively related to nectar sugar content (Fig. S4a, b ). Therefore, the effect of nectar accessibility should only be evaluated when also taking into account the effect of nectar sugar content (i. e. analyses presented with the reduced dataset, Table  S3  and S4 ). For all pollinator groups, plants with longer nectar tubes had a lower probability of sharing pollinators with other plants (Binomial model, Table  S3 ). When sharing pollinators, plants with longer nectar tubes had less influence on other plants (Gaussian model, Fig. 4 ). This pattern held for bees, beetles, butterflies and moths. However, for flies no effect of nectar tube length on mean M€ uller's index was detected, and for bees a All combinations of terms were tested, but only the terms included in the three best models are listed: shape similarity (ShapeSim), phylogenetic distance (PD), acting plant floral abundance relative to the whole community (ARF), target-acting plant floral abundance balance (TAB), acting plant's nectar tube length (TL) and nectar splitting level (NS), visitor density (VD), interaction evenness (IE), plant richness (PR).