dataesr/scientific-paragraphs-categorization huggingface dataset:
g. status, country, institution, email address) regarding users. This information allows the COMPADRE team to notify users if/ when necessary (e. g. new data release, correcting errors, new/ updated R scripts for matrix manipulations and analyses), as well as to obtain user statistics that will help justify grant support for the database in the long term. For these reasons, users should not share the downloaded data, but rather encourage other potential users to register and obtain the latest data set directly from COMPADRE portal. Upon login, users are able to download four files: an R data object file (COMPADRE_Data-Nov_10_2014. RData) that contains the study-specific and matrix-specific information described in Table  1 ; the user's guide, which details how the previous file is organized (Appendix S2); a zip file containing R scripts for data subsetting and manipulation (Appendix S3); and a nexus file containing the phylogeny of the species included in the first file (Appendix S3).
Once a significant volume of studies has been digitized, standardized and error-checked (see Data quality below), that section of the COMPADRE working version is pushed to the COMPADRE portal. Following the initial release of data from 468 studies, at the time of publication of this manuscript, updates of the database will be made publicly available every 6 monthswe currently have a total of 992 species in the process of being digitized and error-checked. For every new release, any errors found in previous releases will be corrected and accompanied by a note in the variable Observation (see COMPADRE User's Guide and Table  1 ). In order to allow scientific reproducibility, outdated versions will be archived and accessible via the COMPADRE portal. Future versions of the COMPADRE portal will allow users to upload information.
0, names provided by the authors are not the currently accepted names (i. e. no match, synonym or unresolved as per The Plant List). Citing the correct taxonomic name is vital to cross-database research (see The vision: beyond COMPADRE, below). Because taxonomic names may be updated frequently, we provide an R script to check on name spelling, synonyms and accepted taxonomic names based on the R package Taxonstand (Appendix S3). • Phylogenetic tree: A phylogenetic tree for species in COMP- ADRE has been constructed to allow users to account for Fig. 2. Workflow of search for matrix population models (MPMs) and ancillary information, digitization, standardization, error-checking and release of information in the COMPADRE Plant Matrix Database. After plant demographic data are collected, used to parameterize MPMs and published by the researchers, (a) publications containing MPMs for plants are found by the COMPADRE digitization team through frequent searches, or personally communicated by researchers, and incorporated in the COMPADRE Plant Matrix Database initial records.
202-218 The COMPADRE Plant Matrix Database 209 phylogenetic ancestry if they wish to conduct comparative analyses (Appendixes S3 and S5). • Geolocation: The location where the demographic informa- tion was collected is important to the interpretation of MPMs. When GPS coordinates were made available, these were checked for consistency with the region and countries where the research took place (e. • Architectural organization: The anatomic and physiological organization of plants can constrain their demography  (Silvertown et al. 1993; Franco & Silvertown 1997; Silvertown, Franco & Harper 1997; Stott et al. 2010; Baudisch et al. 2013; Adler et al. 2014). In COMPADRE, plant habit is characterized according to the classification GrowthType (Table  1 ). • Division of demographic processes: Whenever possible, each projection matrix A in COMPADRE has been partitioned into a submatrix U that contains only transitions and survival of existing individuals, a submatrix F that contains sexual reproduction and a submatrix C that contains clonal reproduction, as described in Fig.
4 ). In addition to 'individual', 'sea-sonal' and 'mean' MPMs, COMPADRE also includes 'pooled' MPMs. The latter is only made available through the author, as they require individual-level data of the study, which COMPADRE does not have (see 'What COMPADRE is not about' below). A pooled MPM results from parameterizing the matrix directly from individual-level data, pooling a set of periods and populations, and thus by-passing the aforementioned element-by-element arithmetic mean approach. Depending on the estimation procedures used, the pooled and mean matrices may be identical if each matrix is based on the same number of individuals; they will, however, tend to differ when the sample sizes are different. The COMPADRE team makes every effort to ensure data accuracy. However, we take no responsibility for consequences of improper use of the database by the user, nor for the possibility of potential typographical errors and omissions.
This is also a particular concern if we are to gain a basic understanding of the intraspecific demographic variability and to implement optimal conservation management measurements. Indeed, some species can exhibit a great deal of demographic variability, which in some cases can be even greater than among non-closely related species  (Oostermeijer et al. 1996; Silvertown, Franco & Menges 2000; Menges et al. 1996; Jongejans et al. 2010). I N F O R M A T I O N N O T C O N T A I N E D I N C O M P A D R E 3. 0 The The potential of COMPADRE is not in the MPMs per se, but in the outputs that can be derived from them. We have made a number of simple R scripts available to manipulate and interact with matrices, derive demographic outputs and correct for phylogenetic relationships (Appendix S3). Users are welcome to explore these or other more developed open-source libraries  (Stubben & Milligan 2007; Stott, Hodgson & Townley 2012; Metcalf et al.
U S E S A N D M I S U S E S Users of COMPADRE, in accordance of scientific ethical standards, are encouraged to acknowledge those who collected the field data, parameterized the MPMs, and made them public. To facilitate study citation, in addition to the variables described in Table  1  under the aspect 'Source', we have also made available an R script that produces a citation list of the studies based on their DOI or ISBN (Appendix S3). Users are also encouraged to visit the Supporting Information Appendix S4 for a comprehensive list of studies made public in the present release. Significant efforts have been made to digitize, standardize, error-check and supplement demographic information with relevant ancillary information such as ecoregion, growth form, taxonomy and phylogeny. Consequently, the information available in the COMPADRE online portal (www.
T H E V I S I O N : B E Y O N D C O M P A D R E COMPADRE contributes to an ambitious programme envisioned decades ago. Much of that vision has been realized, and here, we detail various expansions that we envisage for the future. Some arise from new technologies or analytical methodologies, while others from the increased interest in gathering natural history records of species and the expansion of biological data repositories. Several projects have recently enabled users to obtain the real-time biological information of species observed in natural settings, such as iSpot (http://www. ispotnature. org). Phenological events such as first flowering are recorded by members of the general public and students via online Websites and apps and verified by experts before ingesting into public databases; NaturesCalendar in the UK (http://www.
naturescalendar. org. uk) and ClimateWatch in Australia (http://www. climatewatch. org. au) are two rather successful examples. We believe that COMPADRE has potential for research, teaching and outreach in a similar way as the aforementioned projects. Our vision is that, after some initial training, any user will be able to obtain information on important demographic attributes of a species/ population such as life expectancy, the abiotic and biotic agents that most affect its population dynamics, time to quasiextinction and potential to invade all within the reach of a smartphone with GPS to determine locality and a built-in camera to determine the species of interest and microhabitat conditions. To that end, we are currently developing an R library, COMPADRE-DB (Salguero-G omez & Jones, unpublished data), that interacts with the online portal to manipulate MPMs, derive demographic output, and interact with other databases.
Naturally, this potential requires integration of COMPADRE with other existing data repositories and their interconnectivity through a central, interconnected platform. Central repositories are already available for most of the relevant disciplines: genetics (GenBank), taxonomy (Catalogue of Life; The Plant List), plant anatomic and physiological traits (TRY, BIEN, D3), occurrence data (GBIF), conservation status (IUCN) and climate information (Fetch). What is missing currently is the central platform and the user interface protocols to allow for real-time interactivity. New online repositories such as the Australian Ecological Knowledge and Observation System (http://www. ecoinformatics. org. au) have made progress at integrating rich ecological data at a national level via online tools for data description and publication and serve as a model for future efforts.
The potential of COMPADRE also resides in cross-taxonomic, comprehensive demographic studies. Other databases are available or under development that will help us progress towards this goal: birds [BIDDABA, n = 857 species  (Lebreton et al. 2012) ], mammals {Pantheria [life-history traits of all extinct and recently extinct mammals,  (Jones et al. 2009) ]}, or all animals: DATLIFE (life tables for 293, age-specific fertility for 61, maximum life span for 2659, adult/juvenile mortality for 532 animal species; A. Scheuerlein & Vaupel, unpubl. data), COMADRE (MPMs for over 1300 animal species; R. Salguero-G omez and COMADRE core committee unpubl. data), PADRINO  [Integral Projection Models (IPMs)  for approx. 100 plant species; R. Salguero-G omez, unpubl. data] and the Global Population Dynamics Database (NERC Centre for Population Biology 2010).
2008). Analyses based on the COMPADRE database will also help to identify lacunae in our current understanding of plant demography. For example, comparative analyses would benefit from greater spatial and temporal replication in demographic studies. Variation in demographic parameters within species across years or sites representing its distribution across gradients  (Buckley et al. 2010; Zeigler, Che-Castaldo & Neel 2013 ) may be as large as variation among species  (Jongejans & de Kroon 2005) , limiting ability to draw conclusions or to identify patterns across taxa. In the working version of COMP- ADRE (Fig. 2 ), 37% of studies (365/992) contain data on the same species at two or more sites, and only 23% of studies (229/992) contain data for more than 5 years. Yet, such data are critical for improving inferences and incorporating spatial and temporal stochasticity in analyses of MPMs.
As cross-disciplinary and cross-taxonomic research develops with the contributions of COMPADRE, our team will do all within our reach to remain updated on information and user needs. Scientific methods come and go, and we cannot predict how MPMs will be used in the coming decades, or what new ways of parameterizing or formulating MPMs will become common practice (e. g. vital rates are starting to be parameterized as density-dependent, climate-dependent variables). This uncertainty may pose some challenges but may also create opportunities for COMPADRE and for the users. The database and its associated protocol for digitizing, standardizing and error-checking information will likely change to respond to user needs; researchers are encouraged to provide us with feedback. At the same time, we hope that users will be inspired to maximize the usefulness and impact of their own data by collecting the ancillary information that is standardized in COMPADRE; for instance, we found that most studies do not report GPS coordinates of the study populations  (Johnson & Barton 2004).
To facilitate integration into future versions of COMPADRE, all MPMs and associated metadata should be made available in the publication's supplementary information, or as separate data papers  (Ellis et al. 2012). The ultimate goal of COMPADRE is to encourage plant population ecologists to share information and think not only about their specific questions, but also about how addressing a given question with their specific study species fits into the complex collage of what we know and do not know about plant demography. The 932 species currently in the working version of COMPADRE (Fig. 1 ) represent 0. 374% of the ~250 000 extanct species of plants  (Govaerts 2001). Given how costly these data are to obtain, this is a fairly impressive representation, particularly when considering the substantial phylogenetic coverage in the data set (Appendix S5), and the youth of plant population ecology as a discipline  (Harper 1977).
The exponential increase in the number of these types of studies provides further cause for optimism (Fig. 1 ). Nonetheless, COMPADRE shows gaps and opportunities for the development of research in geographic regions where no plant studies are yet included, for example, in most African countries and the Middle East (Fig. 5d ). One of the great strengths of comparative studies is their use as a tool for identifying generalities that can be extrapolated to poorly known species  (Shea et al. 1998; Ramula et al. 2008). With many plant species threatened with extinction both locally and globally, COMPADRE provides an easily accessible tool for obtaining data for these species, or for identifying useful generalizations for particular taxonomic groups, life-history strategies and regions which should help guide the management and forecasting of threatened populations.
Variable names and meaning contain in the COMPADRE Plant Matrix Database, organized by seven general aspects: taxonomy, plant architecture, source, details of study, geolocation and matrix population model. A more detailed description can be found in the user protocol of Currently accepted taxonomic name as used by The Plant List (www. theplantlist. org). See the Appendix S3 for an R script to check accepted and synonym names from SpeciesAuthor above Authority Taxonomic authority of SpeciesAccepted TaxonomicStatus Whether SpeciesAuthor is currently accepted or synonym of SpeciesAccepted, as per The Plant List TPLVersion Version of The Plant List used for taxonomic validation InfraspecificAccepted Taxonomic intraspecific name of study species, as per The Plant List SpeciesEpithetAccepted Taxonomic species epithet of study species, as per The Plant List GenusAccepted Taxonomic genus of study species, as per The Plant List These may not match the number of populations with MPMs in COMPADRE 3.
(Hintze et al. 2013)& Dixon 2000) plant (and algae) demography. COMPADRE does not contain the individual-level records of survival, changes in stage, reproduction and death from which MPMs are often parameterized. Likewise, COMP- ADRE does not contain integral projection models(Easterling, Ellner & Dixon 2000), as another IPM database is currently under construction (63 plant species, PADRINO; R. Salguero-G omez, unpubl. data). In addition to the MPMs, COMPADRE mostly contains supporting demographic data. Regarding questions for which other information not contained in COMP- ADRE (e. g. reproductive system, microhabitat conditions, seed mass) would be critical, users are encouraged to explore existing databases [e. g. TRY(Kattge et al. 2011), BIEN (http://bien. nceas. ucsb. edu/bien),D3(Hintzeet al.
Acknowledgements A list of authors whose fieldwork and analytical efforts constitute the very core of the COMPADRE Plant Matrix Database is provided in the Supporting Information Appendix S4. The authors' willingness to share unpublished data and clarify questions on published materials is an inspiration for the 'big data, big ecology' era. COMPADRE is currently supported by the  Evolutionary Demography Laboratory  and the  Max Planck Research Group on Modeling the Evolution of Aging  at the  Max Planck Institute for Demographic Research (MPIDR) ; for a list of funding support since its inception, in 1989, see Supporting Information. Logistical and computing support was provided by the IT team at the MPIDR.
g. life expectancy g e , mean age at first reproduction L a , vital rate sensitivities), the MPM A (matA, below) has been split into survival (matU), sexual (matF) and clonal reproduction (matC) submatrices (Fig. 3) when sufficient information was provided in the source. In 4% of cases, insufficient information led to us not been able to split A into U, F and C. SurvivalIssue Reports maximum stage-specific survival in the submatrix U (below). If this value > 1, users are encouraged to carefully evaluate the matrix (continued)their study (Fig. 2), the first step in database digitization is for COMPADRE project leaders and the digitization team to find published MPMs. Information reaches us through two main channels: (i) personal communications (currently < 2%) and (ii) periodic searches implemented by the project leaders in the core committee.
However, both the village and the monastery are threatened by undercutting of the river bank formed by unconsolidated sediments, as it already happened in 2011. Further, the comparison of the GLOF occurrences with temperature and precipitation from the High Asia Reanalysis (HAR) data set for the period 2001-2011 suggests that the GLOF is climate-driven rather than generated by an extreme precipitation event. The calculation of geodetic mass balance and the analysis of satellite images showed a rapid thinning and retreat of Halji Glacier which will eventually lead to a decline of the lake basin. As the basin will persist for at least several years, effective mitigation measures should be considered. A further reinforcement of the gabion walls was suggested as an artificial lake drainage is not feasible given the difficult accessibility of the glacier.
Identification of the lake and mapping of the glacier extent from satellite data In order to identify the source of the flooding we checked a number of satellite image archives for the period from April to September. The number of useful images for this period is limited due to persistent cloud cover related to the monsoon. Thus the lake could be detected only on three Landsat images. To understand the circumstances of the GLOF events, changes in the extent of the Halji Glacier in the last decade were mapped using two cloud-free satellite images from Landsat-7 with a ground resolution of 30 m. The scenes were acquired by the Enhanced Thematic Mapper Plus (ETM+) sensor on 13 October 2001 and 26 November 2011. The glacier body was delimited using a band ratio of bands 3 and 5 (i. e. RED/SWIR) and a subsequent threshold application, which is an effective approach also in shadowed areas  (Paul and Kääb, 2005).
, 2008; Westoby et al. , 2012). In a first processing step, VisualSFM creates a sparse point cloud and shows the approximated camera positions in a graphical interface (Fig. 4a ). In a second processing step a dense point cloud is generated. As no ground control points (GCPs) were used  so far, the dense point cloud is referenced to a relative coordinate system. In order to translate the dense point cloud to a metric coordinate system, we employed seven GCPs acquired by DGPS at the same time that the images were taken. Here we used the freely available SFM-georef Matlab package provided by  James and Robson (2012). As the dense point cloud consists of millions of points we only used the mean value of points on a 1 m × 1 m grid for interpolating the lake basin DEM (LB DEM). Next to the SFM points we included 124 DGPS measurements in the final interpolation (Fig.
Correlation of GLOF occurrence with climate data Hourly precipitation and temperature 2 m above the surface were derived from the High Asia Reanalysis (HAR) data set. The data correspond to a model cell representing the elevation of 5273 m a. s. l. , located 4 km from the glacier to the west (Fig. 1 ). The model contains gridded fields of atmospheric variables with a resolution of 10 km for the Tibetan Plateau and surroundings  (Maussion et al. , 2014). Following the idea of a degree day model  (Braithwaite, 1984)  the hourly above-zero temperatures of the cell were cumulated for the period from January to June for each year between 2001 and 2011. The end of June corresponds to the outburst of the lake. The cumulative temperature thus represents a proxy for the volume of meltwater amount discharging from the glacier to the lake.
The lake basin can fill up relatively quickly during June as documented by two Landsat images for the year 2007. The flood occurred each time within a range of a few days at the end of June/beginning of July. This suggests that the floods are rather climate-driven than triggered by extreme precipitation events. This is further supported by the coincidence of the GLOF events with high values of cumulative abovezero temperatures calculated from the HAR data set. It was shown that the lake basin can almost fill up merely with liquid precipitation accumulated until the end of June. Snow and glacier melting during June also depend on temperature. This means that in each year with a relatively warm June, a GLOF event may occur. The satellite images from May 2013 and June 2014 suggest that during the years without GLOF no lake develops in the basin.
Despite significant advancements in the conception of (formal) integrated development environments, applying formal methods in software industry is still perceived as a difficult task. To make the task easier, providing tools that help during the development cycle is essential but we think that education of computer scientists and software engineers is also an important challenge to take up. Indeed, we believe that formal methods courses do not appear sufficiently early in compter science curricula and thus are not widely used and perceived as a valid professional skill. In this paper, we claim that teaching formal methods could be done at the undergraduate level by mixing formal methods and discrete mathematics courses and we illustrate such an approach with a small development within FoCaLiZe.
For example, in  [13] , the design of a web interface for Coq used to teach logic to undergraduate students is presented. In the context of computer science teaching, formal reasoning is generally introduced at a more advanced stage. This can be done by implementing some automated theorem proving techniques (like in  [11] ) or by using proof assistants such as Coq or Isabelle. In this case, F-IDE and theorem proving are not objects of the study but are rather considered as a framework for teaching something else. Hopefully, using a language as a vehicle for reinforcing concepts inevitably leads to learn some methodological and practical knowledges about it. For example,  [16]  is a semantics textbook (to master students) which is entirely based on the proof assistant Isabelle. The main benefit of using a proof assistant in the teaching of semantics is that it allows students to experiment their specifications and to make proofs by using a computer program, which guides them through the development of a completely correct proof and gives them immediate feedback.