dataesr/scientific-paragraphs-categorization huggingface dataset:
Results In this section, we report on the results of the participants. The raw experimental data and the scripts submitted by participants can be obtained by cloning the repository available at: https://gitlab. inria. fr/crv14/evaluation. For each track, we present the scores obtained in each category and the final scores achieved by each team, as defined in Sect. 5. In the following tables, teams are ranked according to their total scores. Let us recall that the experiments were conducted on DataMill  [73]. The selected machine was queen, which has an Intel(R) Core(TM) i7-2600K CPU @ 3. 40 GHZ (x86_64 architecture with 8 cores), 7. 72 GB of DDR3, and is running on a Gentoo Linux distribution. We have considered the Wall-clock time for our measures. Using DataMill guarantees that each tool had the same execution environment, and it was the only running software during each experiment.
The success of the SAT competitions has inspired other initiatives such as the Satisfiability Modulo Theories Competitions (SMT-COMP)  [2]  started in 2005. The challenge of SMT-COMP is to efficiently check the satisfiability of firstorder formula modulo a background theory. In this case, the chosen theory strictly depends on the nature of the problem to consider (i. e. , arrays, bit-vectors, uninterpreted functions, etc. ). A major challenge for the SMT community has been to devise a common input language for their tools that could accommodate different theories and to express their syntax and semantics. This goal was achieved in 2004 with the release of SMT-LIB a standard input language that is now used as common format for the selected benchmarks in SMT-COMP. One important difference of CRV with SMT-COMP and the SAT competition is the lack of a common input language for the participating tools.
In CRV, a benchmark unit includes a program or a trace and a property to be monitored. Properties can be expressed using different formal specification languages more or less expressive and computationally complex to be monitored. The interplay between the allowed expressiveness and the monitoring complexity plays an important role in CRV competition. Some tools may result extremely efficient in detecting simple temporal behaviors, but then they may lack the necessary support to detect more complex properties and vice versa. One of the open challenges for CRV remains the possibility to have a common formal specification language that is general enough to express all the other common formal specification languages used in the RV community. In the area of software verification, there are three related competitions that have been recently introduced: SV-COMP  [21] , VerifyThis  [55]  and RERS Challenge  [54].
SV-COMP initiated in 2011 within TACAS  [1]  community with the aim to compare tools for software model checking. Benchmarks are provided as C programs, while the requirements to check are provided in terms of linear temporal logic formulas. SV-COMP targets tools for the exhaustive exploration of all program behaviors. On the contrary, CRV is dedicated to monitoring tools analyzing only a single program's execution using runtime and offline verification techniques. In SV-COMP the memory utilization is not taken in consideration and the time needed to verify a property does not affect the program execution itself since the verification process is separated from the program execution. The runtime verification tools competing in CRV introduce instead an overhead for the monitored program and they consume memory resources that could affect the execution of the program 123  itself.
This is the reason why CRV assigns a score to both the overhead and the memory utilization. VerifyThis  [55]  is another series of competitions dedicated to program verification and initiated in 2011. In VerifyThis, the organisers provide to the participants algorithms in a pseudo-code with an informal specification written in natural language. The challenge for each team is to formalize the requirements, implement a prototype and verify whether the implementation is correct w. r. t. the given requirements. The available time to accomplish this goal is quite short ranging between 45 and 90 minutes. The format of VerifyThis competition differs with CRV format because is problem-centred and focuses more on the skills of the team in formalizing and solving the problem rather than on the tool characteristics and performance.
-Choices needed to be made regarding the classification criteria of tracks. Moreover, different tracks could have been possible: domain of the monitored system, programming language of the monitor, categories of specifications, a track on elegance of the specification. The organisers have arranged the tracks of the competition according to the monitored system: either its programming language in case of monitoring software or traces. This reflects the fact inline monitoring has been so far the most popular RV setting when monitoring software. Negative points Several negative aspects are to be noted regarding the first edition of CRV competition. -Significant delays were observed regarding benchmark submission. These delays were due to the substantial efforts required to convey the exact semantics of the specifications submitted.
Even if one has a highly efficient analysis, insufficient coverage can severely limit the effectiveness of a tool at more than a few handful of 1000 lines of code in size. On the one hand, given the maturity of the language, legacy support becomes a concern. The libraries participating in a mature C project may be staggered chronologically, and when delving into the depths, it is not uncommon to encounter rarely used built-in functions like setjmp, keyworded modifiers such as register, and even the use of the goto statement. To its credit, the C language strives to be parsimonious in its extensions, but this also means that it is not uncommon to find highly tailored and difficult to analyze features such as custom-defined memory allocators. Furthermore, many dialects of C, such as those intended for use in embedded environments, often extend the language with compiler-specific and/or platform-dependent constructs.
Acknowledgements Open access funding provided by  TU Wien (TUW). The competition organizers,  E. Bartocci ,  B. Bonakdarpour , and  Y. Falcone , are grateful to many people. The competition organizers would like to warmly thank all participants for their hard work, the members of the runtime verification community who encouraged them to initiate this work, the  Laboratoire d'Informatique de Grenoble  and its  IT team  for its support,  Inria  and its  GitLab framework , and finally the DataMill team for providing us with such a nice experimentation platform to run all benchmarks. All the authors acknowledge the support of the  ICT   COST Action IC1402 Runtime Verification beyond Monitoring (ARVI). Ezio Bartocci  acknowledges also the partial support of the  Austrian FFG  project  HARMONIA  (No.
, 2016). As such, additional analyses are warranted to better characterize the hydrological dimension of the event and mitigate its impacts. In this study, we analyze the European drought of 2015 from a hydrological perspective based on streamflow observations. Such an analysis is challenging for several reasons. First and foremost, the analyses require up-to-date streamflow records across Europe. The pan-European perspective is crucial to studying droughts because a number of hydroclimatological processes act on the continental scale, requiring large-scale datasets to identify regional patterns. However, to date, no publicly available pan-European databases that include near-real-time records exist. Secondly, drought is a spatiotemporal phenomenon. Hence, its dynamic development over space and time, which we herein refer to as the "footprint" of a drought (e.
Data collation strategy Severe droughts are characterized by a large spatial extent and may cover large parts of the European continent  (EEA, 2010). Assessing the hydrological characteristics of droughts therefore requires streamflow data across Europe. However, there are still major barriers in data exchange, which have hindered initiatives to build up international data archives and to perform urgently needed transboundary intercomparison studies  (Hannah et al. , 2011; Viglione et al. , 2010). Existing data archives such as the FRIEND-Water European Water Archive (EWA, http://undine. bafg. de/servlet/is/7413) and the Global Runoff Dataset (http://www. bafg. de/GRDC) at the Global Runoff Data Centre (GRDC) are precious initiatives to make data accessible across Europe. But their content is still limited with respect to their spatial coverage.
Moreover, they are designed as data archives of the past rather than for monitoring in near-real-time. Keeping the data up to date is challenging, and the fact that flow records are often officially released only 2-3 years after recording make these archives inappropriate for a timely assessment of extreme events. For collecting hydrological information from different European countries in near-real-time, a bottom-up strategy was pursued in this study. Instead of collecting stream-flow records, we collect low flow indices for approximately 800 gauges across Europe, which were calculated by partners in the individual countries. It appears easier to do the data processing in the home country and to exchange only derived data (indices), rather than the raw flow data. To ensure consistent derivation of the low flow and drought indices, we have compiled and distributed low flow software.
Ours is open-source software and consists of two packages based on the widely used statistical software R. The first package, termed lfstat  (Koffler et al. , 2016) , provides a collection of state-of-the-art functions to compute a range of low flow characteristics that are fully described in the WMO manual on low flow estimation and prediction  (Gustard and Demuth, 2008). The package has been recently extended to perform extreme-value statistics of both low flow discharges and drought characteristics such as duration and deficit volume. The package uses a robust approach based on L-moments to fit extreme-value distributions  (Hosking and Wallis, 2005). It contains approaches for pooling interrupted events  (Hisdal et al. , 2004)  and for series containing zero values  (Stedinger et al. , 1993).
The second package, termed drought 2015  (Gauster and Laaha, 2016) , builds on lfstat and extends it to perform consistent multi-station analysis. The package employs literate programming enabling all partners to generate dynamic reports that are updated automatically if data or analyses change. We use a common reference period 1 January 1976-31 December 2010 to calculate indices and statistics representing long-term average conditions. The year 2015 is then compared to the characteristics of the reference period and the year 2003. As the end of available records for the year 2015 differs across countries, a common termination date (31 October 2015) was chosen.
Clustering is then performed on the basis coefficients rather than on multivariate observations, which has the benefit of temporal structures being conserved. Analyses are performed using the method fscm from the R-package funcy  (Yassouridis et al. , 2016) , which applies the functional mixed mixture model of  Jiang and Serban (2012)  to perform the clustering. The 2003 and 2015 events are analyzed separately, based on monthly mean discharges of the January-October period. These are converted into a standardized streamflow index (SSI) for each month (e. g. ,  Staudinger et al. , 2015; Barker et al. , 2016)  to make low flow hydrographs comparable across European regimes. For each event, the method returns a classification of hydrographs into groups of similar shape, together with an estimation of the mean hydrograph of each cluster center.
The potential for damage is high as drought events typically affect large areas and may last for a considerable period of time. Although mitigation measures on a European or regional level require timely and accurate information about the physical system, a timely analysis of the hydrological situation is challenging on the continental scale. As described in Sect. 2, there are major barriers in terms of data access, especially for eastern European countries. Wherever data are available, compatibility poses a challenge. All these obstacles were overcome in this study by capitalizing on the potential of a well-established international network, provided by UNESCO's EURO FRIEND-Water program. Without this network, a timely analysis of the event would not have been possible. The resulting collated dataset offers a unique opportunity to analyze the 2015 drought across most of Europe from a hydrological perspective.
Switzerland and southern Germany issued restrictions to a common law that normally allows citizens to extract small amounts of water from rivers to water their gardens  (BAFU, 2015, and exemplary: Stadt Waldkirch, 2015). Impacts on the navigability of larger and thus on waterborne transport were first reported for the Elbe, Weser, and Odra Rivers in late May, for the River Danube from mid-late July onwards, and for the River Rhine from early August onwards  (BfG, 2015). From September onwards, springs dried up in the mountain regions of southern Germany, affecting local water supplies  (DPA, 2015). Characterizing events in a way that is relevant for drought management requires timely pan-European data to be made publicly available. Such data platforms exist for meteorological variables, but similar structures for the exchange of hydrological data are missing and need to be established.
A more targeted large-scale drought monitoring, however, requires hydrological data on a pan-European scale. Such data are available to some extent (see European Drought Observatory; http://edo. jrc. ec. europa. eu), but largely unavailable for free and in near-realtime. Providing the necessary data for managing drought in a proactive way requires a concerted action of the hydrological and climatic communities. Such action should include a pan-European provision of monitored streamflow and groundwater data in real-time or near-real-time, of hydrometeorological variables, and of multi-monthly and seasonal forecasts for both climatic and hydrological variables  (Van Lanen et al. , 2016). The results also highlight the need to implement national and European water policy where additional efforts are undertaken to make near-real-time hydrological data available across borders in order to make drought management more operational in the future.
Acknowledgements. This study was conceived by a team of European drought experts from the  UNESCO   EURO FRIEND-Water Low Flow and Drought network , which enabled collection of near-real-time hydrological data and impact reports across Europe that otherwise, would have been impossible. This research supports the work of the  UNESCO-IHP VIII FRIEND-Water  programme. Data provision by national hydrometeorological services was highly appreciated. Funding from  ACRP  project  DALF-Pro  ( GZ B464822 ) is gratefully acknowledged. The paper is dedicated to  Alan Gustard  ( Institute of Hydrology and Centre for Hydrology and Ecology, Wallingford, UK ), one pioneer of transboundary low flow research who inspired this community effort to overcome data barriers across Europe and beyond. Edited by: L.
A standardized data evaluation procedure was applied, based on five semi-quantitative data quality indicators in the form of a pedigree matrix. This approach assessed the sufficiency of the datasets and allowed the identification of the main sources of uncertainty, highlighting aspects that require further investigation. The overall dataset was found to be sufficient in terms of reliability and spatiotemporal relevance. However, it lacked in completeness, showing that there are still large areas of the Aegean that remain understudied, while further research is needed to elucidate the distribution patterns and conservation status of several ecological features; especially the less charismatic.
Scope of the Study This study was carried out in the framework of the MARISCA project  (2015-2016; www. marisca. eu) whose ultimate goal was to propose a network of MPAs and protection zones in the context of an integrated Marine Spatial Planning approach for the conservation of marine biodiversity and the management of marine-related human activities in the Aegean Sea. To fulfill this goal, this large-scale project focused on combining information of existing ecological, socio-economic, and spatial management data, in order to proceed with the establishment of a transparent system of spatial prioritization using the software MARXAN with Zones  (Watts et al. , 2009). As a first step into this process, this paper presents the ecological mapping and data quality evaluation of key marine features in the area, in order to establish the ecological basis upon which to set up alternative conservation scenarios; the latter will be addressed in future published work.
D. theses. (ii) Past projects-Datasets from past projects provided by universities, research institutes, and non-governmental organizations were scrutinized for unpublished data. (iii) Online databases-Online scientific databases, mapping services, citizen science networks, and diving guides were utilized to recover additional information and maximize the spatial extent of our data. (iv) Interviews and questionnaires-Dedicated questionnaires with embedded images of key species and habitats were specifically designed to collect information from divers, diving centers, university students, marine researchers, and fishers throughout Greece (see details in Supplementary Files 1, 4). (v) Expert judgment-The delineation of important areas for certain wide-ranging, highly mobile species was achieved through collaboration with experts who are engaged in the long-term monitoring of these populations.
(vi) Remote sensing-Medium resolution satellite images (Landsat-8) were used to acquire new data for marine habitats of the infralittoral zone (<30 m depth; see Supplementary File 1 for a detailed description of the methodological framework). (vii) New field data-During 2016, a series of dedicated field expeditions were organized in order to validate and update existing records, as well as to explore understudied areas at selected sites of the Aegean Sea. The expeditions included SCUBA diving surveys for the investigation of hard substrates at depths shallower than 40 m, use of high resolution side scan sonar for seabed imaging, Unmanned Aerial Vehicles (UAVs) surveys for validation of satellite images, Remotely Operated Vehicles (ROVs), and underwater drop camera dives for ground-truth sampling and assessment of the deeper bathymetric limits of certain habitats (e.
g. , P. oceanica beds and coralligenous formations). The respective New field data were obtained from a total of 135 sites sampled during these expeditions (see Supplementary File 1 for analytical information on the field methods). A detailed description of the data sources used per ecological feature, alongside the corresponding methodological considerations and data assumptions, are provided in the paragraphs that follow. Priority habitat types are marked with an asterisk. The percentage of records that is included in designated marine parks or Natura 2000 sites is indicated in the last two columns.
Coastal lagoons Coastal lagoons refer to all expanses of shallow coastal salt water which are wholly or partially separated from the sea by the deposits of sand, shingle or rocks (as defined by the 92/43/EEC), also including those that are found in the proximity of deltaic systems. Geographic information on this habitat type was extracted from general purpose catalogs, scientific documents (e. g. ,  Nicolaidou et al. , 2005 ; General Administration for Sustainable Fisheries, 2016), online databases (WWF, 2016), and Google Earth.
Marine habitats of the infralittoral zone (<30 m)-seagrass beds, soft substrates and hard substrates Satellite images were used to acquire new data for the mapping of three generic habitat types; seagrass beds (consisting primarily of P. oceanica, but also some Cymodocea nodosa meadows), soft substrates (containing sandy or muddy sediments, small pebbles, and organogenic/detritic substrates), and rocky substrates found in waters shallower than 30 m. Spatial data for the aforementioned habitat types were extracted from Landsat-8 satellite images using image processing techniques. The Landsat-8 habitat-classified images were delivered as geo-referenced data with a 30 m (pixel size) spatial resolution, and ∼10 m positional accuracy. Aerial inspection utilizing a UAV (i. e. , quadcopter), ground-truth sampling, and experts' knowledge, were used to validate the acquired information at selected sites.
, 2014; Dimas et al. , 2015; Geraga et al. , 2016) , and ecological assessments (e. g. ,  Sini et al. , 2015; Supplementary File 4). New information on coralligenous formations of the littoral rock found at depths shallower than 40 m was acquired through interviews and questionnaires with divers, and during the SCUBA diving surveys of the present study. Regarding coralligenous platforms, new data were collected based on the re-evaluation of information obtained during past projects utilizing geo-acoustic systems (see technical reports in Supplementary File 4); this information led to the selection of specific locations for ROV targeted dives at the southern parts of the Cyclades plateau, which enabled the ground-truthing of past acoustic data, and offered further insights regarding the existence of coralligenous platforms in the study area.
The "corals of the sublittoral zone" dataset refers to all arborescent corals whose occurrence in the Aegean Sea is primarily known from waters shallower than 200 m. These include nine gorgonian species (Eunicella cavolini, E. singularis, E. verrucosa, Paramuricea clavata, P. macrospina, Leptogorgia sarmentosa, Spinimuricea klavereni, and Villogorgia bebrycoides), as well as the red coral Corallium rubrum. According to the IUCN Red List of threatened Mediterranean Anthozoans  (Otero et al. , 2017) , the aforementioned Eunicella species are ranked as Near Threatened, P. clavata as Vulnerable, C. rubrum as Endangered, while P. macrospina, S. klavereni, and V. bebrycoides as Data Deficient. The main part of the corresponding dataset comprises information obtained from scientific literature and interviews with SCUBA divers.
Corals of the bathyal zone (CBZ) This dataset includes information on all arborescent coral species that are found at depths greater than 200 m (i. e. , the bathyal zone). Distribution data were extracted from few scattered records found in the scientific literature  (Pérès and Picard, 1958; Zibrowius, 1979 Zibrowius, , 1980;; Vafidis et al. , 1994; Smith et al. , 2009; Salomidi et al. , 2010)  and online databases. These refer to the rare occurrences of individual specimens of the scleractinians Cladopsammia rolandi, Dendrophyllia cornigera, D. ramea, as well as the gorgonians Isidella elongata, Swiftia pallida, and S. dubia; Isidella elongata is considered to be Critically Endangered, D. cornigera as Endangered, D. ramea as Vulnerable, while C. rolandi and S. pallida as Data Deficient  (Otero et al.
Protected Species Porifera The Porifera dataset contains information on nine taxa of conservation interest (Table  1 ). Most of the information comes from scientific literature, as well as from new records of field surveys and interviews conducted during the present study. Scientific literature includes research that specifically focuses on sponges (e. g. ,  Voultsiadou, 1986; Kefalas and Castritsi-Catharios, 2007; Voultsiadou et al. , 2008 Voultsiadou et al. , , 2010;; Gerovasileiou and Voultsiadou, 2012) , as well as studies with a broader ecological scope (e. g. ,  Pérès and Picard, 1958; Antoniadou et al. , 2006; Salomidi et al. , 2016). Scattered records found in the technical reports of past projects and online databases were also considered, but account only for a small fraction of the dataset.