dataesr/scientific-paragraphs-categorization huggingface dataset:
e. flying) stages. Although information on some dispersal-related traits such as body size, reproduction, locomotion and dispersal mode is available in online databases for European  [18] [19] [20]  and North American taxa  21  , other relevant information is scattered across published literature and unpublished data. Using the expertise of 19 experts, we built a comprehensive database containing nine dispersal-related traits divided into 39 trait categories for 480 European taxa. Dispersal-related traits were selected and their trait categories fuzzy-coded  22  following a structure comparable to existing databases  23. Our aim was to provide a single resource facilitating the inclusion of dispersal in ecological research, and to create the basis for a global dispersal database.
Data acquisition and compilation A taxa list was generated based on existing trait databases  20  and modified to reflect current taxonomic classifications. Trait information was sourced primarily from the literature using Google Scholar searches of keywords including trait names, synonyms and taxon names, and by searching in existing databases  18, 21. Altogether, >300 peer-reviewed articles and book chapters were consulted (Supplementary File 1). When no European studies were available, we considered information from other continents only if experts evaluated traits as comparable across regions. When published information was lacking, traits were coded based on authors' expert knowledge and direct measurements. Specifically, for 139 species in 69 genera of Coleoptera and Heteroptera, female wing lengths were characterized using measurements of 538 individuals in experts' reference collections, sampled in Finland, Hungary and Greece.
Data Records DISPERSE can be downloaded as an Excel spreadsheet from the Intermittent River Biodiversity Analysis and Synthesis webpage  30  or Figshare at: https://figshare. com/s/4f074fb4bdbbe8cf149e. The database comprises three sheets: DataKey, Data and Reference list. The "Datakey" sheet summarizes the content of each column in the "Data" sheet. The "Data" sheet includes the fuzzycoded trait categories and cites the sources used to code each trait. The four first columns list the taxa and their taxonomy (group, family, genus [or lowest taxonomic resolution achieved including e. g. tribes and sub-families] and genus synonyms) to allow users to sort and compile information. Sources are cited in chronological order by the surname of the first author and the year of publication. Expert evaluations are reported as "Unpublished" followed by the name of the expert providing the information.
Direct measurements are reported as "Direct measurement from" followed by the expert's name. The "Reference list" sheet contains the references cited in the "Data" sheet, organized in alphabetical order and then by date (also available in Supplementary File 1). In total, the database contains nine dispersal-related traits divided into 39 trait categories for 480 taxa. Most (78%) taxa are insects, principally Coleoptera and Trichoptera, as these are, together with Diptera, the most diverse orders in freshwater ecosystems  31. DISPERSE provides complete trait information for 61% of taxa, with 1-2 traits being incomplete for the 39% remaining taxa (Table  1 , Figure  2 ). The traits with the highest percentage of information across taxa were wing pair type and maximum body size, followed by dispersal strategy, life-cycle duration, potential number of reproductive cycles per year, and female wing length (Table  1 ).
Technical Validation Most of the trait information (88%) originated from published literature (Supplementary File 1) and the rest was coded based on expert knowledge (9%) and direct measurements (3%) (Table  1 ). The database states information sources for each trait and taxon, allowing users evaluate data quality. Most traits were coded using multiple sources representing multiple species within a genus. When only one study was available, we supplemented this information with expert knowledge, to ensure that trait codes represented potential variability in the taxon. Using insects as an example, we performed a fuzzy correspondence analysis (FCA)  22  to visualize variability in trait composition among taxa (Figure  3 ). Insect orders were clearly distinguished based on their dispersal-related traits, with 32% of the variation explained by the first two FCA axes.
Wing pair type and lifelong fecundity had the highest correlation coefficient (0. 87 and 0. 63, respectively) with axis A1. Female wing length (0. 73) and maximum body size (0. 55) best correlated with axis A2 (Figures  3  and 4 ). For example, Coleoptera typically produce few eggs and have intermediate maximum body sizes and wing lengths, Odonata produce an intermediate number of eggs and have long wings, and Ephemeroptera produce many eggs and have short wings (Figures  1  and 4 ). The database currently represents a Europe-wide resource, which can be updated and expanded as new information becomes available, to include more taxa and traits from across and beyond Europe. For example, additional information could be collected on other measures of wing morphology  10, 14  and functionality or descriptors of exogenous dispersal vectors such as wind and animals  32.
Usage Notes DISPERSE is the first publicly available database describing the dispersal traits of aquatic macroinvertebrates including information on both aquatic and aerial (i. e. flying) life stages. It provides good coverage of macroinvertebrates at the genus level, which is generally considered as adequate to capture biodiversity dynamics  [34] [35] [36] [37]. It will promote incorporation of dispersal proxies into fundamental and applied population and community ecology in aquatic ecosystems  5. In particular, metacommunity ecology may benefit from the use of dispersal traits  15, 38  , which enable classification of taxa according to their dispersal potential in more detail. Such classification, used in combination with, for example, spatial distance measurements  39, 40  , may advance our understanding of the effects of regional dispersal processes on community assembly and biodiversity patterns.
Users should note that dispersal-related traits included in DISPERSE represent an indirect measure of dispersal, not actual dispersal. Therefore, the database is not intended to substitute population-level studies related to dispersal, but to act as a repository that collates and summarizes information from such studies. As freshwater biodiversity declines at unprecedented rates  53, 54  , collecting, harmonizing and sharing dispersal-related data on freshwater organisms will underpin evidence-informed initiatives that seek to support the resilience of ecosystems adapting to global change.
Acknowledgements The study was supported by the  COST Action CA15113 Science and Management of Intermittent Rivers and Ephemeral Streams  (http://www. smires. eu/),funded by  COST (European Cooperation in Science and Technology). NC was supported by the  French research program Make Our Planet Great Again. MC-A was supported by the  MECODISPER  project ( CTM2017-89295-P ) funded by the  Spanish Ministerio de Economía, Industria y Competitividad (MINECO) -Agencia Estatal de Investigación (AEI)  and cofunded by the  European Regional Development Fund (ERDF). AC-R acknowledges funding by  MINECO-AEI-ERDF  ( CGL2014-53140-P ). CG-C was supported by the  EDRF  ( COMPETE2020  and  PT2020 ) and the  Portuguese Foundation for Science and Technology , through the  CBMA strategic program   UID/BIA/04050/2019  ( POCI-01-0145-FEDER-007569 ) and the  STREAMECO  project (Biodiversity and ecosystem functioning under climate change: from the gene to the stream,  PTDC/CTA-AMB/31245/2017 ).
We present a survey of operational methods for walkability analysis and evaluation, which we hold show promise as decision-support tools for sustainability-oriented planning and urban design. An initial overview of the literature revealed a subdivision of walkability studies into three main lines of research: transport and land use, urban health, and livable cities. A further selection of articles from the Scopus and Web of Science databases focused on scientific papers that deal with walkability evaluation methods and their suitability as planning and decision-support tools. This led to the definition of a taxonomy to systematize and compare the methods with regard to factors of walkability, scale of analysis, attention on profiling, aggregation methods, spatialization and sources of data used for calibration and validation.
• The method of data collection (direct survey (D) or provided by external sources (E) such as census database, open data, user generated contents apps); scale of observation (street level, block, census tract, catchment area, neighborhood, city-level). These qualities were used respectively as indicators of multidimensionality, accuracy and precision of the measurement method and its suitability. According to the basic subdivision into four families of factors, the selected walkability evaluation methods revealed that even though, overall, many studies of walkability transversally touch all the families, thus proving to adopt a multidimensional approach, single methods differ in the emphasis given to certain families and in the way of operationalizing the measurement. Some studies focus mainly on the safety and efficiency of space  [81, 85] , others look at the attractiveness of the city  [1, 28]  and still others are more sensitive to pleasantness  [7, 10, 86, 87].
Calibration and Validation Another criterion of our taxonomy is whether the method has been calibrated and validated, or if it is at least suggested how that can or should be done. This aspect is indicative of the validity and reliability of models and hence of their transferability. Calibration and validation are two different steps. Calibration allows us to estimate parameters (criteria, scales, weights, etc. ) of the evaluation model, while validation is the comparison of results of a calibrated model against observed data. Both stages imply the comparison between the model and the real system observed. For this purpose, the availability and the quality of data used to describe the real functionality of the studied context becomes crucial. We examined walkability studies and made a distinction based on the kind of data the authors use (or recommend using) for the calibration and validation: revealed behaviors (RB), stated preferences (SP), or a panel of experts (EX).
The examined methods presented a prevalent tendency to collect data in the field, although a number combine external sources data gathering (mainly from open source datasets) with direct collection. Many recent tools resort to some form of automated data collection and big data analytics. We believe automation represents one of the new frontiers of the research on walkability models  [21]. The increasing use of open geodata contributes to broadening the availability and accessibility of basic information, making data collection and analysis procedures simpler and replicable. Spatial web services and social network platforms for example enable the collection of both objective and subjective data more quickly and easily. New and growingly ubiquitous technologies (e. g. , GPS tracking, sensors, social networks data, open data) are useful tools for conferring smartness to decision-support tools and urban policy making.
Such technologies can support the collection of information on the micro-level that cannot be gathered without expensive efforts by simple audit tools or existing administrative datasets. In sum, open source geodata and software increase both the operationality and usability of walkability assessment methods. The ability to encompass and integrate both objective and subjective information is another important requirement of walkability-oriented planning support systems. Our investigation has shown that subjective data are usually used for model calibration and validation, while the attempt to customize the analytical and assessment functions as well as the results according to the intrinsic characteristics of the users (profile of individuals or social groups) and places (catchment areas, neighborhoods, city, regions) is limited only to a few tools analyzed.
Bromeliad aquatic ecosystems are inhabited by a diverse fauna  [10] [11] [12]  , comprising top predators, mesopredators, and detritivores  15, 24, 25. We contrasted rainfall-mediated changes in hydrological stability of the study system with the effects of two main rainfall components: (i) the mean daily amount of rainfall, μ; and (ii) distribution of rainfall events around this mean through time, k (i. e. , a measure of evenness in the frequency distribution of rainfall; hereafter "frequency"; Fig. 1b ). For instance, reductions in both mean daily rainfall (low μ) and the even frequency of rain (low k, hereafter "infrequent rainfall") increase the proportion of days that bromeliads are empty of water. Regular (current) variability of the rainfall components in each site were first determined using recent meteorological data (see Methods).
e. , the number of days water depth was ≥maximum depth recorded divided by the total number of measurements, (iii) proportion of dried-out days, i. e. , the number of days water depth was <5 mm divided by the total number of measurements. These hydrological parameters were used to create a metric of hydrological stability, using Principal Component Analysis (PCA)  15. We used the scores of the first axis of the PCA (Supplementary Table  2 ), for each site, to summarize these parameters into a single variable of hydrological stability. This axis quantified a gradient of habitat permanence and stability, where increasing scores represent more stable ecosystems (i. e. , ecosystems that dried out less often, and held more water throughout the experiment, Supplementary Table  2 ). At the end of the experiment (60th day), we recorded with hand-held data loggers water turbidity, a measure that integrates organic and inorganic suspended matter, including free-living algae and particulate nutrients (C, N, P), resulting from detritivore activity.
Acknowledgements We acknowledge financial support for research provided by the  Saõ Paulo Research Foundation  (FAPESP: grants  2012/51143-3 ,  2017/09052-4 , and  BPE Proc. 2016/01209-9 ) and  CNPq  through research grants to G. Q. R, by the  Agence Nationale de la Recherche (ANR) ,  Rainwebs project  (grant  ANR-12-BSV7-0022-01 ) to R. C. , C. L. , B. C. , J. F. C. and D. S. S. and an  Investissement d'Avenir grant  ( Labex CEBA, ref. ANR-10-LABX-25-01 ), by the  Agencia Nacional de Promocioń Cienti ́fica y Tecnoloǵica  (grant  PICT-2010-1614 ) and  Secretari ́a de Ciencia y Tecnologi ́a de la Universidad Nacional de Rosario  (grant  AGR-139 ) through grants to I. B. and G. M. , by the  Brazilian Council for Research, Development and Innovation (CNPq)  for research funds (Pesquisador Visitante Especial,  PVE, Research Grant   400454/2014-9 ) and productivity grants to V.
F. F. , by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through  Discovery and Accelerator grant  to  D. S. S. , and the  Facultad de Ciencias, Universidad de los Andes, Colombia  through a grant (2012-1) to F. O. This research was further supported by scholarship and fellowship support from the  ANR  to M. K. T. , from  Consejo Nacional de Investigaciones Cienti ́ficas y Tećnicas (CON-ICET)  to R. F. , from  FAPESP  to P. A. P. A. (Proc. 2014/04603-4 ,  2017/26243-8 ), from  Coordenacaõ de Aperfeicoamento de Pessoal de Ni ́vel Superior (CAPES)  to A. B. A. C, J. S. L. , and  N. A. C. M. ( PNPD-CAPES 2013/0877 ) and P. M. O. ( PNPD-CAPES 2014/04603-4 ), an  Investissement d'Avenir grant  ( Labex CEBA, ref. ANR-10-LABX-25-01 ) and a PhD fellowship from the  Fond Social Europeén  to O.
D. , from  NSERC  to A. A. M. M. (CGS-D) and D. S. S. ( EWR Steacie Memorial Fellowship ), from the  University of British Columbia  to S. L. A. , and from the  Departamento Administrativo de Ciencia, Tecnologi ́a e Innovacioń (COL-CIENCIAS) support of F. O. B. (COLCIENCIAS  grant  567 ). G. Q. R. and P. K. gratefully acknowledge funding from the  Royal Society, Newton Advanced Fellowship  (grant no. NAF/ R2/180791 ). E. J. O'G. gratefully acknowledge funding from  NERC  ( NE/L011840/1 ). This paper is a contribution of the  Brazilian Network on Global Climate Change Research  funded by  CNPq  (grant # 550022/2014-7 ) and  FINEP  (grant # 01. 13. 0353. 00 ).
Multi-Agent Simulations We follow, as in the Battle of the Exes benchmark  [18] , a 2x2 between-subjects experimental design. One dimension represents the ballistic and dynamic versions of the game, whereas the other dimension is composed of the high and low difference between payoffs. Each condition is played by 50 agents who are paired in dyads and play together 50 rounds of the game if they are in one of the high payoff conditions (ballistic or dynamic), or 60 rounds if they are in one of the low payoff conditions. Regarding the task, we have developed the two versions (ballistic and dynamic) of the Battle of the Exes in a 2D simulated robotic environment (see Figure  3B  for a visual representation). The source code to replicate this experiment is available online at: https://gitlab. com/specslab/neurorobotics/ control-reinforcement-learning.
We introduce the open-source tool noisi for the forward and inverse modeling of ambient seismic crosscorrelations with spatially varying source spectra. It utilizes pre-computed databases of Green's functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto-and cross-correlations, which have become preeminent seismological observables, in light of nonuniform ambient seismic sources. Written in the Python language, it is accessible for both usage and further development and efficient enough to conduct ambient seismic source inversions for realistic scenarios.
, 2019) , only a modest number of studies have presented models of ambient noise cross-correlations themselves, i. e. , numerical evaluations of cross-correlations due to distributed noise sources rather than models of Green's functions (e. g. ,  Nishida and Fukao, 2007; Tromp et al. , 2010; Hanasoge, 2013a; Basini et al. , 2013; Ermert et al. , 2017; Sager et al. , 2018b Sager et al. , , 2020;; Datta et al. , 2019; Xu et al. , 2018 Xu et al. , , 2019)). Several state-of-the-art open-source tools for ambient noise data processing are freely available, e. g. , MSnoise  (Lecocq et al. , 2014) , FastPCC  (Ventosa et al. , 2019) , yam (https://github. com/trichter/yam, last access: 7 July 2020), and NoisePy (https: //github. com/chengxinjiang/Noise_python, last access: 7 July 2020). However, the same cannot be said about cross-correlation modeling tools, which have mostly been developed ad hoc by different research groups  (Hanasoge, 2013a; Fichtner, 2014; Sager et al.
, 2020; Xu et al. , 2019 ). An exception is the openly available implementation of noise cross-correlations and sensitivity kernels in SPECFEM3D  (Tromp et al. , 2010) ; however, in its current form it is not tailored to the exploration of different noise source models and their impact on cross-correlation. Moreover, it requires high-performance computing (HPC) resources for many applications. Therefore, we present a tool named noisi for modeling ambient noise cross-correlations while honoring the physics of wave propagation and for determining source sensitivity kernels which can be used for rapid, cross-correlation-based ambient noise source inversion. The tool is implemented in Python, parallelized using mpi4py  (Dalcín et al. , 2005) , and provided on GitHub alongside a tutorial and an exemplary ambient noise source inversion setup.
Using waveform databases for rapid, realistic cross-correlation models One of the main challenges in modeling ambient noise crosscorrelations is the adequate representation of seismic wave propagation from the noise sources, which are in general globally distributed  (Stehly et al. , 2006; Nishida and Takagi, 2016; Retailleau et al. , 2018) , to seismic receivers. The noise cross-correlation implementations of  Tromp et al. (2010)  and  Sager et al. (2018a)  honor the physics of wave propagation to the greatest possible extent but require substantial HPC resources for inversion  (Sager et al. , 2020). The noisi tool uses databases of pre-calculated seismic wavefields in-stead to compute cross-correlations and sensitivity kernels. It therefore presents an alternative for cross-correlation modeling and noise source inversion for cases where updates to the structure model (i.
e. , seismic velocities, density, and attenuation) are not required. Owing to the reuse of Green's functions, computation is quick and inexpensive. However, storage resources, typically on the order of 1 GB per station, are needed to hold the Green's function database. Databases of pre-calculated Green's functions have recently been applied to a variety of seismological problems, such as source inversion of earthquakes  (Dahm et al. , 2018; Fichtner and Simutė, 2018) , landslides  (Gualtieri and Ekström, 2018) , and ambient noise  (Ermert et al. , 2017; Datta et al. , 2019). Although the generation of such databases themselves often requires HPC resources, they can be shared to provide access to the results of costly wave propagation simulations to users without access to those resources. This is achieved, for example, by the IRIS Synthetics Engine (Syngine) repository  (IRIS, 2015; Krischer et al.
, 2017)  and by tools for the extraction and management of Green's function databases  (van Driel et al. , 2015; Heimann et al. , 2019). The noisi tool enables the use of Syngine databases for modeling noise cross-correlations. However, it is not limited to these; rather, pre-calculated Green's function databases from any numerical wave propagation solver, which may include 3-D Earth structure, topography, etc. , can be used with noisi after appropriate formatting.
For the evaluation of Eq. (  4 ), source-receiver reciprocity  (Aki and Richards, 2002)  is invoked, G j m (x 2 , ξ , ω) = G mj (ξ , x 2 , ω), (5) so that a point force source can be placed at the location of one seismic receiver, and the Green's functions to any source at the Earth's surface is recorded, which is far more practicable than simulating waves from a large number of possible seismic noise source locations to the receiver. If an Earth model is assumed a priori, e. g. , the Preliminary Reference Earth Model  (Dziewoński and Anderson, 1981)  or another model resulting from seismic tomography, the obtained Green's functions G in (x 1 , ξ , ω) and G j m (x 2 , ξ , ω) are fixed throughout the simulation or inversion, and Eq. (  4 ) can be evaluated multiple times while requiring only one potentially costly wave propagation simulation per receiver or none if prepared databases such as the ones from Syngine are used.
(2010) ,  Hanasoge (2013a ), Fichtner (2014) ,  Ermert et al. (2017) ,  Sager et al. (2018b), and Xu et al. (2019). The noisi tool computes both forward model and sensitivity kernels. It has been constructed to fulfill both tasks in a simple, flexible, and computationally inexpensive way, and addresses them as follows. Noise sources are treated as spatially varying power spectral densities according to Eq. (  3 ). Wave propagation Green's functions G in are read from a database in HDF5 format  (Folk et al. , 2011). The tool includes setup routines for (i) analytic surface wave Green's functions, (ii) Green's functions for spherically symmetric Earth models provided by instaseis  (van Driel et al. , 2015)  with wavefield databases hosted by the Syngine repository  (Krischer et al. , 2017) , and (iii) Green's functions for laterally varying Earth models obtained from AxiSEM3D  (Leng et al.