dataesr/scientific-paragraphs-categorization huggingface dataset:
Anthozoa The protected Anthozoa dataset includes information on seven strictly protected taxa; one hexacoral and six octocorals (Table  1 ). The bulk of the dataset consists of records regarding C. caespitosa, the only endemic scleractinian coral of the Mediterranean known to form reefs in the infralittoral zone  (Laborel, 1987). C. caespitosa distribution data were primarily obtained through interviews and field observations conducted in the present study, while additional information originated from past projects or scientific literature. Available data on the remaining six octocorals were restricted to four scientific documents  (Vafidis et al. , 1994; Salomidi et al. , 2009 Salomidi et al. , , 2010;; Taviani et al. , 2011)  and few scattered records from online databases and interviews.
Mollusca The Mollusca dataset involves nine protected species; six gastropods and three bivalves (Table  1 ). Purely scientific data sources (i. e. , scientific literature, field observations during past or present projects) and information provided through interviews with experts or non-experts had an overall equal contribution to this dataset. However, with the exception of Pinna nobilis, which is the most commonly studied/recorded mollusk considered herein (e. g. ,  Katsanevakis et al. , 2008; Katsanevakis and Thessalou-Legaki, 2009; Vafidis et al. , 2014) , little dedicated research has been conducted or published on the remaining species, for which distribution data is primarily anecdotal.
Echinodermata Data on the two protected Echinodermata species, Centrostephanus longispinus and Ophidiaster ophidianus, were mainly collected through interviews with divers. Additional records were obtained from past projects and new field data, while relevant information in scientific documents was restricted to few observations reported during wider biodiversity assessments. Osteichthyes-Hippocampus spp. The geographic distribution of the two Hippocampus species, H. guttulatus and H. hippocampus, was mainly based on information extracted from fisheries surveys conducted by the Hellenic Centre for Marine Research (HCMR;  Petrakis et al. , 2001 Petrakis et al. , , 2009) )  over the time period 2000-2001 (8 months) and 2008-2009 (6 months). All observations were made onboard commercial boat seiners targeting Spicara smaris at depths ranging from 5 to 40 m.
Benthic Elasmobranchii (Sharks, rays, and skates) Data originate from a series of fishery-dependent and independent surveys conducted since the 1990's by HCMR. Fishery-dependent surveys were realized during the period 2002-2015 (with some annual gaps) in the framework of the national obligation to monitor the fisheries sector based on the requirements of the EU Common Fisheries Policy (Data Collection Regulation-COM 1543/2000; Data Collection Framework-COM 199/2008). Additional fishery-independent surveys were conducted through several projects financed by national or international funds focusing on the benthic communities of Greek waters (see Supplementary File 4 for a full list of projects). The most long-standing fisheries monitoring project is MEDITS  (Bertrand et al. , 2000) , implemented since 1994 in all EU Mediterranean countries following a common protocol.
Cetacea Seven cetacean species are resident in the Aegean Sea (Table  1 ;  Frantzis, 2009). Three of them have their Mediterranean population classified as Endangered (Physeter macrocephalus, Delphinus delphis, Phocoena phocoena relicta) in the IUCN Red List  (Reeves and Notarbartolo di Sciara, 2006). Spatial information on the distribution of the seven cetacean species was provided by the Pelagos Cetacean Research Institute. Specifically, the delineation of important areas for each of the seven species was based on distribution data obtained through field observations over the last 15 years, habitat suitability modeling  (Giannoulaki et al. , 2016) , and expert judgment (for optimal polygon limits). The aforementioned field observations include geo-referenced sightings of cetacean groups which were mainly recorded during dedicated visual-acoustic surveys, or as opportunistic observations accompanied by photographic material for the identification/verification of species.
Data Mapping To enable the construction of illustrative maps and the comprehensive evaluation of the data sources, all information was digitized and organized into a collective geographic database using the Pan-European grid-ETRS89 Lambert Azimuthal Equal Area projection coordinate reference system. Separate feature classes (points or polygons) were created for each ecological feature. To illustrate the number of ecological features found in different parts of the Aegean Sea, a heat map was constructed using a 2 × 2 km LAEA grid for national waters, and a 10 × 10 km grid for international waters, corresponding to a total of 29,628 grid cells covering the entire study area. The proportion of the different ecological features that were recorded within existing Marine Parks and Natura 2000 sites (the EU network of protected areas) was estimated based on the total number of records found within or outside these areas for point data, and total area cover for polygon data.
Data Quality The aim of the data quality assessment was to evaluate the origin of the data, identify the main sources of uncertainty, and highlight aspects that need further improvement. Data evaluation is a critical step of the conservation planning process, as data quality may influence the reliability of the analyses and the interpretation of the respective outcomes  (Levin et al. , 2014). However, the systematic evaluation of datasets presents several challenges, as various aspects that affect data quality are arbitrary (e. g. , habitat definitions, geographic position, spatial/temporal extent) and hence suffer from intrinsic biases  (Ciroth, 2009). Moreover, metadata are usually expressed in a qualitative manner or their documentation is insufficient. It is therefore difficult to extract a meaningful result regarding the overall data reliability, especially when a large amount of datasets is combined.
One way to overcome this hurdle is the use of a pedigree matrix which transforms qualitative metadata into semi-quantitative indicators of data quality. This approach was originally proposed by  Weidema and Wesnaes (1996)  and was later tested by  Issaris et al. (2012)  on data used for ecological mapping. Following the methods employed by the aforementioned studies, a modified pedigree matrix was applied to evaluate the reliability, completeness, spatiotemporal cohesion, and data acquisition methods of the datasets used for the mapping of the distribution of ecological features (Table  3 ). Each quality indicator was assessed based on five indicator-specific scoring criteria, ranking from 1 (high quality) to 5 (low quality). The "reliability" indicator relates to the data sources and the verification procedures used (e.
g. , species identification process) to obtain the data; the "completeness" indicator relates to the statistical properties of the data (the spatial and temporal representativity of samples); the "temporal relevance" indicator represents the year the data were obtained relative to the year of the data quality assessment; the "geographical relevance" refers to the level of accuracy of the obtained geographic positioning metadata. Additionally, the "data acquisition methods" indicator relates to the methods used to collect primary data (research focus and applied methodology). The scoring procedure was applied separately for distinct ecological features and the resulting values were graphically displayed using radar charts (Figure  5 ). Indicator scores were calculated using the weighted average of all data sources considered.
3% of entire study area) for soft substrates, 1,590 km 2 (0. 7%) for seagrass beds, and 164 km 2 (0. 08%) for rocky substrates. Data quality was high for the seagrass beds and soft substrate habitats, as the spatial resolution of satellite images was considered to be sufficient for the visual identification of these habitat types. On the contrary, complementary experts' judgment and groundtruth sampling indicated that the mapping of hard substrates is, as yet, incomplete. Marine Caves (Figure  2B ) A total of 622 marine caves have been recorded within the study area. Most of the point data (85%) correspond to semisubmerged caves, the majority of which (70%) are located in the island-dominated S Aegean region, while only 7% stands for marine caves found along the mainland coastline. According to the results, marine caves appear to be more abundant in the Cyclades, the southeastern Aegean, and the Northern Sporades island complexes, where limestone coasts prevail.
Submarine Structures Made by Leaking Gases-SSLG (Figure  2B ) The presently known distribution of this habitat is restricted to four points, which are located nearby volcanic hotspots along the Aegean volcanic arc. Taking into account the high volcanic and tectonic activity of the wider Aegean region, this dataset cannot be considered as complete. Rhodolith Beds (Figure  2C ) The distribution of rhodolith beds is described by 62 point data and a total polygon area cover of 72 km 2. Available data refer to rhodolith beds mainly found in waters deeper than 40 m, with the Cyclades plateau being the most extensively studied area. In the rest of the Aegean, there are important data gaps, while the majority of records (57%) refer to data obtained more than 15 years ago. Coralligenous Formations (Figure  2C ) Available information consists of 326 point data and a total polygon area cover of 71 km 2.
In the S Aegean, this coralligenous morphotype is mainly scattered along the coasts of the insular areas. Data quality is overall low in terms of completeness, as dedicated research for both morphotypes is generally lacking in most parts of the study area. Protected Anthozoa, Corals of the Sublittoral Zone, and Corals of the Bathyal Zone (Figure  2D ) The distribution of corals of the sublittoral zone is described by a total of 169 point data. Most observations come from the N Aegean (84%), and particularly from the Chalkidiki and Pelio peninsulas, as well as from the Northern Sporades and Evia islands. Of the protected Anthozoa dataset, the scleractinian coral C. caespitosa (143 points) also presents an overall northwest to southeast decreasing trend, showing a higher frequency of records in the areas of the Evoikos Gulf (25%), Chalkidiki Peninsula (20%), Lesvos Island (13%), and Pelio Peninsula (13%).
These refer to H. perlo (67%), G. galeus (29%), and I. oxyrinchus (4%). Records of sharks can be found throughout the Aegean Sea. However, their density is higher at the southern insular areas, especially around the Cyclades and Dodecanese island complexes. Information gaps can be observed along the southeastern coasts of Crete and at several eastern Aegean islands, and mainly reflect differences in fisheries-dependent scientific effort. Benthic Elasmobranchii (Figure  3B ) The dataset includes 439 point data. The rays Gymnura altavela and Mobula mobular comprise 7% of the data points, the skates Dipturus batis, Leucoraja circularis, L. melitensis, and Rostroraja alba 24%, the guitar fish Rhinobatos rhinobatos 1%, the torpedo ray Tetronarce nobiliana 45%, and the demersal sharks Hexanchus griseus, Oxynotus centrina, Squatina aculeata, S.
Data Quality Scores The results of the data quality evaluation, based on the five qualitative indicators, are presented in Table  4  and Figure 5. As a general rule, low-scoring indicators represent good data quality, while high-scoring indicators refer to weak points that should be taken into account during any future decision-making process. The weakest indicator of the overall dataset is "completeness," showing that there are still several areas of the Aegean Sea that remain understudied, and that the distribution of certain ecological features needs further investigation. On the other hand, most of the available data are sufficient in terms of "reliability," "temporal relevance," and "geographical relevance. " The ecological features with the highest-scoring valuesand hence highest uncertainty-were the taxa of Porifera, Mollusca, and Echinodermata, due to the fact that their datasets were largely based on information provided by interviews.
DISCUSSION This study is the first to assess, at a large scale, the spatial distribution of key marine habitats and species in the Aegean ecoregion. A wide range of data sources and methodological approaches was utilized to address the versatility of ecological features and update the presently available scientific records. Moreover, the systematic assessment of data quality provided useful insights regarding the credibility of the obtained information, allowed the detection of potential data deficiencies or inaccuracies, and helped identify information gaps that need further investigation. Such quality assessment is also useful in weighing the different lines of evidence when the data is to be used within integrated assessment schemes, as is common in ecosystem-based management approaches.
Future research should focus on addressing this knowledge gap by using higher spatial resolution satellite images (e. g. , Sentinel-2) and by creating a robust open-access database for further validation and reference; this data repository should also incorporate information on the geomorphological characteristics, position, and bathymetric extent of rocky areas. Complementary to the data provided by  Giakoumi et al. (2013) , the current cave dataset provides information on 120 additional cave locations (20% increase), summing up to a total of 622 caves that are scattered throughout the study area. Given the complexity of the Aegean coastline and the ongoing geodynamic processes, it is almost impossible to provide an exact number of marine caves existing in this ecoregion, but the actual number is assumed to be much higher.
g. , the Hellenic Rescue and Information Network-RINT for the Mediterranean monk seal; the National Rescue Network for sea turtles). Complementary to scientific research, the production of well-informed and illustrated identification guides, and the strengthening of observatory networks, will foster the acquisition of more reliable information from both expert scientists and nonexpert naturalists  (Costello et al. , 2010). Once dealt with in a systematic manner (standardized and validated), this source of information can greatly enhance our knowledge on the past and present distribution patterns of key marine habitats and species. Citizen science is receiving growing attention as an effective tool to collect ecological information at large spatial scales  (Bonney et al. , 2009; Silvertown, 2009)  and can be further utilized to update and improve the present dataset.
Species occurrence data may also be incorporated into global species databases, online repositories, and ongoing initiatives focusing on the cataloging and sustainable management of biodiversity (e. g. ,  Bailly et al. , 2016) , which is a common requirement for all signatories of the CBD. At the same time, trans-boundary collaboration is necessary to achieve comprehensive and environmentally meaningful conservation planning for the entire Aegean ecoregion, as ecologically important areas extend beyond territorial waters. The role of international conventions and agreements along with joint research initiatives and scientific networks can be pivotal to this end  (Giakoumi et al. , 2012a; Levin et al. , 2014). The expansion of EU policies and regulations, such as the Marine Strategy Framework Directive (MSFD) and the Natura 2000 network, outside the strict EU borders through the Barcelona Convention may further promote common baseline data and research practices.
In Section 2, we introduce the CMSSM, NUHM1 and NUHM2, along with their parameters, the ranges and priors over which we vary those parameters, and the algorithms and settings that we use for sampling them. Section 3 contains a summary of the experimental data, observables and likelihood calculations that go into each fit. We then present our results in Section 4, before looking at the implications of our scans for future searches for the models in question, and concluding in Section 5. All input files, samples and best-fit benchmarks produced for this paper are publicly accessible from Zenodo  [163].
High-energy neutrinos from dark matter annihilation in the Sun The Sun is expected to capture neutralinos from the local halo by nuclear scattering. Subsequent neutralino annihilation and interaction of the annihilation products in the solar core would produce GeV-energy neutrinos, which may be detectable at the Earth. The rate-limiting step for all MSSM models is the capture process, which depends sensitively on both the spin-dependent and spin-independent nuclear scattering cross-sections. The dominant constraints on intermediate and high-mass neutralino annihilation in the solar interior currently come from the IceCube experiment  [223, 224]. We use the DarkBit interface  [156]  to nulike 1. 0. 4 [7, 225] , which computes an unbinned likelihood from the event-level energy and angular information contained in the three independent event selections of the 79-string IceCube dataset  [223].
Marginalising over ξ this way, it is possible to include the effects of fractional systematic uncertainties on both the signal prediction (σ s ) and the background estimate (σ b ). 5 e assume a log-normal distribution for ξ, P (ξ|σ ξ ) = 1 √ 2πσ ξ 1 ξ exp - 1 2 ln ξ σ ξ 2 , ( 16 ) where σ 2 ξ = σ 2 s + σ 2 b. We compute this integral using the highly-optimised implementation in nulike 1. 0. 4 [7, 225]. The analyses listed above are statistically independent, either because they use a completely independent dataset (based on collisions at ATLAS versus CMS, or during Run I versus Run II), or because they utilise signal regions that have no overlap with the signal regions of any of the other searches. This allows us to simply multiply the likelihoods of all analyses in order to arrive at a combined likelihood.
Direct detection Now we turn to a discussion of the discovery prospects at future direct DM detection experiments. In Figs. 19  and 20, we show the spin-independent (SI) and spindependent (SD) nuclear scattering cross-sections of the lightest neutralino as a function of its mass, scaled for the fraction of the local density of DM in neutralinos. We give the full profile likelihood for the CMSSM only. The other panels show 2σ confidence regions for each model, with colour-coded mechanisms for reducing the relic density to or below the observed value. In the CMSSM, the chargino co-annihilation and A/H-funnel regions largely overlap, predicting SI crosssections in the range 10 -46 -10 -44 cm 2 and neutralino masses from 200 to 2500 GeV. This region will be almost fully probed by XENON1T after two years of datataking (long dashed curve), and could be completely excluded by XENONnT or LZ with 1-3 years of data (short dashed curve).
In the same figure we also indicate, for comparison, current limits from dwarf galaxy observations by the Fermi-LAT  [221] , assuming photon spectra for DM annihilation to bb and τ + τ -final states. We also show projected Fermi limits for bb final states  [346] , assuming 15 years of data on 60 dwarf galaxies (vs 6 yr and 15 dwarfs in the current limits). Lastly, we show the projected sensitivity of CTA after 500 hours of observation of the Galactic halo, also assuming bb final states  [347]. We note that the actual (projected) limits depend on the final state, and hence the lines shown are only indicative for general points in the CMSSM parameter space. However, as long as the final states are hadronic, the expected variations remain within a factor of about three  [348]. In general, the largest annihilation cross-sections are expected for the A/H funnel region, where resonant annihilation boosts σv.
The NUHM1 and NUHM2 plots also show that the same situation holds for stau co-annihilation at m χ 1. 5 TeV in the NUHM, which at such masses appears only in combination with the heavy Higgs funnel. Colour-coding shows the active mechanism(s) by which CMSSM models avoid exceeding the observed relic density of DM, through either chargino co-annihilation, the A/H funnel, or stop co-annihilation. Top Right: Colour-coded regions in the NUHM1, now also featuring stau co-annihilation (blue). Bottom Right: Colour-coded regions of the NUHM2. 95% CL exclusion limits are overlaid from the 6-year Fermi-LAT search for DM annihilation in 15 satellite dwarf galaxies  [221] , assuming dark matter annihilation to bb (yellow solid) and τ + τ -(red solid) final states. We also show the projected improvement for b b final states with 15 years of LAT data and four times as many dwarfs  [346]  (dashed yellow), and an optimistic projection of the sensitivity to b b final states of a Galactic halo search for DM annihilation by the upcoming Cherenkov Telescope Array, assuming 500 hr of observations and no systematic uncertainties  [347]  (green dashes).
Acknowledgements We thank  Andrew Fowlie ,  Tomás Gonzalo ,  Julia Harz ,  Sebastian Hoof ,  Felix Kahlhoefer ,  James McKay ,  Roberto Trotta  and  Sebastian Wild  for useful discussions, and  Lucien Boland ,  Sean Crosby  and  Goncalo Borges  of the  Australian Centre of Excellence for Particle Physics at the Terascale  for computing assistance and resources. We warmly thank the  Casa Matemáticas Oaxaca , affiliated with the  Banff International Research Station , for hospitality whilst part of this work was completed, and the staff at  Cyfronet , for their always helpful supercomputing support. GAMBIT has been supported by  STFC (UK ;  ST/K00414X/1 ,  ST/P000762/1 ), the  Royal Society (UK ;  UF110191 ), Glasgow University (UK; Leadership Fellowship), the  Research Council of Norway  ( FRIPRO 230546/F20 ), NOTUR (Norway;  NN9284K ), the  Knut and Alice Wallenberg Foundation (Sweden; Wallenberg Academy Fellowship) , the  Swedish Research Council  ( 621-2014-5772 ), the  Australian Research Council  ( CE110001004 ,  FT130100018 ,  FT140100244 ,  FT160100274 ),  The University of Sydney (Australia ;  IRCA-G162448 ),  PLGrid Infrastructure (Poland) ,  Red Española de Supercomputación  (Spain;  FI-2016-1-0021 ),  Polish National Science Center (Sonata   UMO-2015/17/D/ST2/03532 ), the  Swiss National Science Foundation  ( PP00P2-144674 ), the  European Commission Horizon   2020 Marie Skłodowska-Curie actions  ( H2020-MSCA-RISE-2015-691164 ), the  ERA-CAN+ Twinning Program  ( EU & Canada ), the  Netherlands Organisation for Scientific Research  ( NWO-Vidi 680-47-532 ), the  National Science Foundation (USA ;  DGE-1339067 ), the  FRQNT (Québec)  and  NSERC/The Canadian Tri-Agencies Research Councils  ( BPDF-424460-2012 ).
Background: Amplicon deep sequencing permits sensitive detection of minority clones and improves discriminatory power for genotyping multi-clone Plasmodium falciparum infections. New amplicon sequencing and data analysis protocols are needed for genotyping in epidemiological studies and drug efficacy trials of P. falciparum. Methods: Targeted sequencing of molecular marker csp and novel marker cpmp was conducted in duplicate on mixtures of parasite culture strains and 37 field samples. A protocol allowing to multiplex up to 384 samples in a single sequencing run was applied. Software "HaplotypR" was developed for data analysis. Results: Cpmp was highly diverse (H e = 0. 96) in contrast to csp (H e = 0. 57). Minority clones were robustly detected if their frequency was >1%. False haplotype calls owing to sequencing errors were observed below that threshold.