dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental descriptors (bioclimatic variables). Our modelling strategy relies on a set of bioclimatic descriptors hosted in the Worldclim database  61. Each variable is available in the form of a raster map and represent the average climate conditions for the period 1970-2000. We used raster layers of 2. 5-minute spatial resolution, which corresponds to about 4. 5 km at the equator. The choice of environmental descriptors to be involved is crucial and it is widely acknowledged that using reduced number of variables improves transferability and avoids model overparametrization  62, 63. We used the variables referred to as bio5, bio7 and bio19 corresponding to the maximum temperature of the warmest month, the temperature annual range and the precipitation of the coldest quarter see details in  64.
The NeighborNet network inferred from the concatenation of the reduced sequences of leuA, petC, malF, cysG, holC, nuoL, gltT targeted by the nested PCR approach and including reported STs, available genomes as well as Corsican strains characterized on more than one locus is presented in Fig. 4b. All sequences have been deposited on Genbank (accession numbers: MH628341-MH628361).
Acknowledgements We thank  B. Legendre  from the  ANSES LSV (Angers, France)  for providing an inactivated bacterial suspension to test the sensitivity of our methods. We thank  M. -A. Jacques  ( INRA, IRHS, Angers, France ) for helpful discussion as well as  F. Casabianca  ( INRA, Corte, France ) and  L. Hugot  ( CNBC Corse, France ) for their collaboration in setting up a research project in Corsica. We are indebted to  C. Lannou  ( INRA, SPE, France ) for his interest and support during the course of the project. We are grateful to the anonymous reviewer who reviewed our manuscript. This work was funded by grants from the  SPE department of the INRA (National Agronomic Institute) , the  Territorial Collectivity of Corsica  and the  Horizon 2020 XF-ACTORS  Project  SFS-09-2016. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
g. , population vs. community). Clearly, more research is needed into how populations, communities and ecosystems are changing in the face of widespread human influence on the planet  (Waters et al. , 2016). Here, we introduce BioTIME, a curated database of biodiversity time series, with the goal of facilitating and promoting research in this area. Biodiversity is a multifaceted concept, which can be measured in many different ways. Similar to the approach of essential biodiversity variables  (Pereira et al. , 2013) , we focus on assembling data that maximize the number of metrics that can be calculated. Specifically, BioTIME is composed of species abundance records for assemblages that have been sampled through time with a consistent methodology. The focus on assemblages differentiates BioTIME from population databases, such as the Global Population Dynamics Database (https://www.
imperial. ac. uk/cpb/gpdd2/ secure/login. aspx) and the Living Planet Index database (http://www. livingplanetindex. org/home/index), and enables users to quantify patterns at different organizational levels, including both the assemblage and the population level. BioTIME complements the PREDICTS database (http:// www. predicts. org. uk/) in providing time series rather than space for time comparisons. Moreover, most previous databases have been either terrestrial (e. g. , vertebrates, GPDD; vegetation, sPlot; multiple taxa, PREDICTS) or marine (e. g. , OBIS), whereas BioTIME includes marine, freshwater and terrestrial realms; hence, it facilitates comparisons across realms. (c) to promote appropriate crediting of data sources. These goals are in line with best practice in promoting maximal use of ecological data  (Costello et al.
| M ET HOD S The BioTIME database is composed of 11 tables: a main table containing the core observations (records), and 10 tables that provide contextual information as described below and in Supporting Information Figure  S1. There are five main levels of organization: record, sample, plot, site and study. A record is our fundamental unit of observation of the abundance of a species in a sample. A sample includes all the records that belong to the same sampling event; for example, a quadrat on the seashore, a single plankton tow or a bird transect. A sample is defined by a single location and a single date. If the exact location has been repeatedly sampled through time, then all the samples that correspond to that location belong to the same plot. Multiple samples and plots can be located in the same area, which we term a site.
Finally, the highest observational unit is a study, which is defined by having a regular and consistent sampling methodology. Sources of data in which the sampling methodology changed during the course of the study were classified as separate studies. Every organizational level has contextual variables that are kept either in dedicated tables or are part of the main table (see Supporting Information Figure  S1  for a complete list of the fields in each table). In addition, the database also includes tables with information relating to the sampling methodology, and treatments associated with some samples when applicable, citation information, contacts and licenses for each study, and the curation steps performed on each study before it was entered in the database.
| Data acquisition Searches began in 2010, and data were acquired from a variety of sources: literature searches, large databases [specifically, OBIS (www. iobis. org/), GBIF (www. gbif. org/) and Ecological Data Wiki (https://ecologicaldata. org/)], through personal networking and through broadcasted data requests at conferences and on social media. We have used four main criteria for data inclusion on BioTIME: (a) abundance observations come from samples of assemblages where all individuals within the sample were counted and identified (i. e. , assemblage rather than population data); (b) most of the individuals were identified to species; (c) sampling methods were constant through time; and (d) the time series spans a minimum of 2 years. The last condition was changed relative to the initial criteria because it became apparent that it would allow better spatial representation given the many locations that have been surveyed historically and then resurveyed.
Each study is kept separate within the database and has a specific license from the CC spectrum, whose terms must be observed (https://creativecommons. org/). A static version of the database is released with this publication (http://biotime. st-andrews. ac. uk and https://zenodo. org/record/1095627). However, data entry and curation is ongoing (http://biotime. st-andrews. ac. uk/contribute. php), and we expect the database to keep growing in the foreseeable future. We plan to release static updates of the database periodically.
| Data curation and quality control Before inclusion in the database, data were subjected to standardization in a curation process described specifically for each study in the curation table of the database. Specifically, these were checked for the presence of the following: duplicates within each study and against the entire database; species with zero abundance; and non-organismal records, all of which were removed. Abundances of zero for a particular population can be inferred from their absence from samples in the study. Additionally, species names were checked for typographic errors and misspellings, and a standardized notation was used for records of morphospecies and species complexes. Most records were included as provided and may not always conform to the latest nomenclature. Furthermore, latitudes and longitudes were checked for their location relative to other descriptors (e.
g. , country or marine vs. terrestrial). Finally, the grain and extent of each study were calculated from information in the methods where available, or by applying a convex hull algorithm to locations of the samples. (2014) displays only small differences, despite the database having more than tripled its size in the interim. It is our hope that this geographical bias will decrease over time via targeted searches and data recruitment. There are 44,440 taxa in BioTIME. The majority of these (88. 8%) are species, but some organisms are identified only to coarser taxonomic levels, such as genus. BioTIME includes assemblages across the animal and plant kingdoms, ranging from mammals to microscopic plankton. As with the spatial distribution, there are also taxonomic biases in the data in BioTIME (Figure  2 ).
Almost 70% of records fall into one of four categories: terrestrial plants, birds, fish and marine invertebrates, with fish accounting for 28% of the total database. BioTIME records span 118 years (from 1874 to 2016), with the longest time series having 97 years and an average duration of 13 years. In more detail, 56. 5% of studies contain up to 10 years of data, 42% between 10 and 50 years and 1. 4% > 50 years.
| U S A GE NOT ES Version 1. 0 of the BioTIME database can be downloaded from https:// zenodo. org/record/1095627 or from http://biotime. st-andrews. ac. uk/. The use of data contained in BioTIME should cite original data citations in addition to the present paper. There is considerable variation in the spatial and temporal grain and extent among studies, which must be considered in any analysis of BioTIME data. Moreover, the number of samples was often not constant through time within studies; consequently, we recommend the use of sample-based rarefaction and provide R code to query the database, implement sample-based rarefaction and calculate a suite of biodiversity metrics. Specifically, we provide a tutorial guiding users to interact with both formats of the database  (. csv and. sql; Allaire et al.
, 2015; Becker, Wilks, & Brownrigg, 2014; Oksanen et al. , 2013; Ooms, James, DebRoy, Wickham, & Horner, 2015; R Development Core Team, 2013; Wickham, 2009; Wickham & Francois, 2015). Please note that for interacting with the. sql version of the database, users will have to set up a connection with the server where they have installed the SQL database. For interacting with the. csv version, users have to download both the data and the metadata csv files, making sure that all the paths to these files are modified accordingly. The data included in the present paper represent the subset of data within the BioTIME database for which we were able to secure licences to republish. The additional studies held in the full database have been obtained from publicly available data and are listed in Supporting Information Table  S1.
In total, BioTIME currently holds 387 studies, containing 12,623,386 records from a total of 652,675 distinct geographical locations, and 45,093 species. These records span a total of 124 years from 1858 to 2016 inclusive. We will continue to interact with data providers in order to increase data availability and to recruit additional data. Instructions on how to contribute to future releases can be found here (http://biotime. standrews. ac. uk/contribute. php).
Finally, previous databases are not specifically focused on temporal assemblage data, which means that BioTIME fills an important gap in allowing spatial and temporal comparisons. In addition, coupling BioTIME with additional information will allow analyses of temporal change in phylogenetic diversity and trait diversity alongside taxonomic diversity. The goals of the BioTIME database are as follows: (a) to assemble and format raw species abundance data for assemblages consistently sampled through time; (b) to encourage re-use of these data through open-source access of standardized and curated versions of the data; and.
3 | D ESCR I P TI ON OF DA TA In total, the version of BioTIME released with this paper includes 8,777,413 records, across 547,161 unique locations, gathered from 361 studies (Figure 1; see Appendix for a full list of citations). These observations span the Poles to the Equator, from depths of c. 5,000 m to elevations of c. 4,000 m above sea level, and include the terrestrial, freshwater and marine realms. The database includes records spanning 21 out of 26 ecoregions [WWF; (http://www. worldwildlife. org/biomes)]. Nonetheless, there are spatial biases in the distribution of sampling locations, with most studies occurring in Europe, North America and Australia. This geographical bias has persisted despite the growth of the database. For example, a comparison between Supporting Information Figure S2 and the data included in the study by Dornelas et al.
ACKNOWLEDGMENTS European Research Council  and EU: A. E. M. , M. D. and F. M. are grateful for the support of the  ERC grants BioTIME  [AdG-250189] and  BioCHANGE  [PoC-727440]. J. P. and M. E. acknowledge the financial support from the  ERC Synergy  grant  ERC-SyG-2013-306 610028 IMBALANCE-P , Spanish  CGL2016-79835-P  and Catalan SGR-2017-1005. Long-term sampling of Calafuria rocky shores (L. B. -C. ) has been supported by various E. U. projects, in addition to the  University of Pisa  and the  Census of Marine Life. Natural Environmental Research Council : C. W. is grateful for the support of the  Natural Environmental Research Council  [grant number  NE/ L002531/1 ]. The  Porcupine Abyssal Plain Sustained Observatory  is funded by the  U. K. Natural Environment Research Council. We thank the  Atlantic Meridional Program  (supported by the  U.
K. Natural Environment Research Council  through the  Atlantic Meridional Transect consortium)  and the L4 programme (funded under the  U. K. NERC Oceans 2025 programme as part of Theme 10 , Sustained Observations). C. E. W. thanks the  U. K. 's Natural Environment Research Council  for funding the  Western Channel Observatory 's plankton time-series through the  National Capability programme. National Science Foundation  (NSF): S. K. M. E. acknowledges the  U. S. National Science Foundation  for funding data collection. S. R. S. was supported by  NSF  grant  1400911. The research of F. P. D. was funded by  NSF  grant  DEB-1237733. D. A. K. thanks the  National Science Foundation  (most recently  DEB-1456729 ) for their support. This material (R. D. H. ) is based upon work supported by the  National Science Foundation  under Grant No.
The  U. S. Forest Service (Department of Agriculture)  and the  University of Puerto Rico  gave additional support. J. E. D. thanks the  U. S. National Science Foundation  for support with grants  OCE 95-21184 ,  OCE-0099226 ,  OCE 03-52343 ,  OCE-0623874 ,  OCE-1031061  and  OCE-1336206. Data compilation and cleaning by A. H. H. , B. S. E. and S. J. S. was funded by  NSF  grant  DEB-1354563  to A. H. H. W. A. G. thanks the  AON ITEX program  (awards  1432982 ,  0856710  and  1504381 ). All research at the  U. S. Forest Service International Institute of Tropical Forestry  is done in collaboration with the  University of Puerto Rico. National Science Foundation (LTER) :  Jornada LTER ,  Research Site Manager -New Mexico State University. Datasets were provided by the  Jornada Basin Long-Term Ecological Research (LTER)  project.
J. F. C. acknowledges funding from  NSF  ( DEB-0823293 ) from the  LTER  to the  Coweeta LTER Program at the University of Georgia. Other funding: L. H. A. was supported by  Fundação para a Ciência e Tecnologia, Portugal  ( POPH/FSE SFRH/BD/90469/2012 ). A. R. K. is funded by  Ciências sem Fronteiras and Coordenação de Pessoal de Nível Superior (CAPES, Brazil) , Grant/Award Number:  1091/13-1. R. E. is grateful for support by  Instituto Milenio de Oceanografía   IC120019. A. H. B. is grateful for  ARC Centre of Excellence  (Grant  CE0561432 ). R. P. V. is currently supported by a doctoral grant from  Fundação para a Ciência e Tecnologia, Portugal  ( SFRH/BD/84030/2012 ). J. J. is grateful for funding for data collection from  NSERC Canada. J. R. G. is grateful for the support from  CONICYT/FONDECYT  no.
1160026 ,  ICM PO5-002 ,  CONICYT/FPB-23. A. P. M. is grateful for the support of  FONDECYT  Grants  11110351  and  1151094. A. A. and V. O. thank  RSF  ( 14-50-00029 ). E. P. W. is supported by the  Gordon and Betty Moore Foundation 's  Data-Driven Discovery Initiative Grant   GBMF4563. J. M. A. was supported by  FI/FIAP  ( 1998FI-00596 ) and BE (2001BEAI200208) fellowships from the  Catalan Government (DURSI)  during the fieldwork and by a  MECD-Post-doctoral fellowship  ( EX2002-0022 ), a  Marie Curie Individual Fellowship  ( QLK5-CT2002-51518 ) and  Marie Curie  project  MARIBA  ( MERG-CT-2004-022065 ) afterwards. A. F. receives a scholarship from  CNPq  ( 306170/2015-9 ); G. D. receives a scholarship from  FAPESP  ( 2015/ 10714-6 ); projects to collect data received financial support from  FAPESP (São Paulo Research Foundation ) ( 2015/06743-0 ,  2008/ 10049-9 ),  CNPq  ( 475434/2010-2 ) and  DFG (German Research Foundation , Project  PF 120/10-2 ).
This study was also supported by the  Long Term Ecological Programme (PELD programme -CNPq   403809/2012-6 ) and by  FAPERJ  ( Thematic Programme , process  E-26/110. 114/2013 ). G. B. G. S. was funded by  CAPES/Brazil. C. M. is grateful for the support of the  German Academic Exchange Service (DAAD)  and the  German Research Foundation (DFG). H. B. and U. J. acknowledge the support of sDiv, the  Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. C. F. J. M. , R. R. and A. L. -B. acknowledge funding from  Fundação para a Ciência e Tecnologia, Portugal  ( PTDC/BIA-BIC/111184/2009 ,  SFRH/BD/80488/2011  and  PD/BD/52597/2014 , respectively). F. Z. F. was funded by  CAPES/ Brazil. T. J. W. acknowledges support from the  New Zealand Department of Conservation.
General acknowledgments: Bioinformatics and Computational Biology analyses were supported by the  University of St Andrews Bioinformatics Unit , which is funded by a  Wellcome Trust   ISSF award  (grant  105621/Z/14/Z ). We would like to acknowledge  Richard Osman  for his work in the  Woods Hole study , and the  Smithsonian Atherton Seidell Fund , which provides funds within the  Smithsonian  to make old studies and publications more available. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the.
Different For the specific case of motion generation, it has been recently proposed by  Brandao et al. (2017)  to use a scenario called "Disaster Scenario Dataset". It allows benchmarking posture generation (solved by the WBC) and trajectory generation (MPWBC) using optimization. A set of problems is proposed by means of foot steps locations(F RF , F LF ). From this it is possible to compare algorithms realizing the two functionnalities (WBC and MPWBC). The evaluation is realized in simulation using the Atlas robot and the ODE dynamic simulator. This first step is necessary but one step further is to benchmark a real humanoid platform. For this paper we used a more systematic decomposition of the humanoid bipedal locomotion  Torricelli et al. (2015). Further description can be found in paragraph 4.
General purpose The goal of the Koroibot project was to enhance the ability of humanoid robots to walk in a dynamic and versatile way, and to bring them closer to human capabilities. As depicted in Fig. 3 -(left), the Koroibot project partners had to study human motions and use this knowledge to control humanoid robots via optimal control methods. Human motions were recorded with motion capture systems and stored in an open source data base which can be found at https://koroibot-motion-database. humanoids. kit. edu/. With these data several possibilities were exploited: Criteria that humans are assumed to minimize using Inverse optimal control. Transfer from human behaviors to robots was done with walking alphabets and learning methods  Mandery et al. (2016). These human behavior was safely integrated in robots applying optimal controllers.
We also included an interaction term between management intensity gradient (PCA axis 1) and position in the field, and between soil characteristics (PCA axis 2) and position in the field. We used post hoc tests to investigate the pairwise differences between treatments using least-squares means (the function lsmeans of the R package lsmeans;  Lenth 2016). Thirdly, we tested the role of deterministic drivers (e. g. , competition) vs. stochastic drivers (e. g. , random species loss) of changes in local weed assemblages in the presence and absence of crops in plots without N fertilizer and weed control. We first characterized the b-diversity between each pair of plots within each field using both incidence-based (Jaccard) and abundance-based (Bray-Curtis) compositional dissimilarity metrics (observed b-diversity).
, 2006). Nevertheless, assessments also routinely use expert knowledge  (McBride et al. , 2012) , so that guidelines for the reliable integration of such knowledge are under development  (McCarthy et al. , 2004; McBride et al. , 2012; Drescher et al. , 2013; Drolet et al. , 2015). Publication of both data and assessments is now consolidated at global scale through to the on-line searchable IUCN databases accessible via the Internet at http://www. iucnredlist. org. Assessments are constructed using explicitly defined categories and quantitative criteria that are applicable and valid at global scales  (Akçakaya et al. , 2000; IUCN, 2001). Over the past two decades, these criteria and categories have been revised, thresholds have been adjusted and new categories created  (Gärdenfors, 2001; IUCN, 2013).
One hypothesis suggests that some lists and evaluations may depend primarily on data from grey literature, which conflicts with the comprehensive, scientifically rigorous and transparent nature of Red Lists  (Mrosovsky and Godfrey, 2008). To evaluate whether variability in conservation status between different lists is linked to the differences in the type of information that were synthethized, the proportions of categories of information used in French Red Lists were compared. On the basis of the reported greater likelihood of subjectivity in small-scale assessments  (Eaton et al. , 2005; Keller et al. , 2005) , we predicted that grey literature would play a more important role in small-scale Red Lists than in large-scale assessments. The distinction between scientific and grey literature has been widely debated  (Schöpfel, 2006; Mrosovsky and Godfrey, 2008)  and is detailed below in the methods section.
Study species Highly mobile species may be more affected by the challenges posed by regional adaptations to the IUCN system, particularly when applied at small geographical scales and when data on across-boundary population dynamics are required  (Akçakaya et al. , 2000; Keller and Bollmann, 2004; Keller et al. , 2005; IUCN, 2012). To ensure comparability, analyses should be based on overlapping sets of species; as well, data sets should contain well-studied species in order to make quantitative analyses possible. Many birds are widely distributed mobile species that represent a well-studied taxonomic group  (van Jaarsveld et al. , 1998; Butchart et al. , 2004; Fazey et al. , 2005). However, it is a vast taxonomic group and so we chose to analyse a subset for potential heterogeneity between Red Lists.
Bibliographical categories The references cited in the Red Lists were compiled and classified in four categories on the basis of the following definitions. The first category (A) is 'scientific literature' sensu stricto, that is, work published in scientific journals that is indexed in scientific data sources  (Björk et al. , 2010)  and peer reviewed  (Steven et al. , 2011). Scientific literature meets methodological standards  (Conn et al. , 2003) , is easily available  (Pyšek et al. , 2008)  and represents, notwithstanding certain flaws, a widely accepted strategy for ensuring quality control in scientific research  (Ferreira et al. , 2015). The second category (B) includes 'referenced books', in particular, books identified by an International Standard Book Number (ISBN) and referenced academic publications, such as PhD theses.