dataesr/scientific-paragraphs-categorization huggingface dataset:
Density and temperature profiles Density and temperature data were used for model validation. We used density profiles (bulk densities) measured on the deep ice cores KCC, KCI, KCH, CC and KCS (Fig. 5 ; data from the glaciology group database of the Institute of Environmental Physics (IUP), Heidelberg University). Temperature profiles are shown in Figure  2  and include a selection of deep and shallow temperature profiles measured at CG between 1982 and 2015  (Haeberli and Funk, 1991; Hoelzle and others, 2011; Bauder and others, 2017). The absolute uncertainty of the measurements is up to ±0. 2 K  (Hoelzle and others, 2011).
10a ). However, the very low accumulation rates and submerging velocities predicted by the model in the highest part of the KCC and KCS catchment areas are still too high to produce the bend-like features observed in the respective layercounting chronologies. At the same time, other factors, such as systematic shifts in surface net accumulation at CG during climate periods like the Little Ice Age, may contribute to temporal accumulation variability, that is at present not considered by the model. Extending the age estimates beyond the experimental dating of  Bohleber and others (2018) , at KCC we observe a distinct shift to younger radiocarbon ages just below 60 m. This enigmatic feature of the radiocarbon dataset is still awaiting further investigation  (Hoffmann and others, 2018). Accordingly, deriving a full explanation of this feature is beyond the scope of this work.
Additional merit would come from long-term observations on variability of the velocity and temperature field, glacier topography and surface accumulation. Future investigations should also comprise a firn-core array dedicated to the flowline of KCC and KCI. A key result of this study is the calculation of backward trajectories of the existing drill sites. This allows us, for the first time, to quantitatively investigate upstream effects of the cores KCC and KCI. This provides an important background for the interpretation of existing and future results from time series analysis of these two cores. In particular, knowledge of the upstream catchment area is important to assess the role of the bergschrund influence in various datasets, as demonstrated here for the potential discontinuity of the micro-radiocarbon age profile of KCC and the basal anomaly signals in the stable water isotope profiles of the CG cores.
Code and data availability The source code of Elmer/Ice is available at http://elmerice. elmerfem. org. Calculations were performed using the Elmer version 8. 2 (Rev: a5adc1d). All files required to run the model can be downloaded from https://github. com/carlolic/GnifettiFS. Calculated ice-core chronologies, source points and measured borehole inclination angles are published in the open-access database PANGAEA and available at https://doi. pangaea. de/10. 1594/PANGAEA. 906016.
Acknowledgments. We gratefully thank the  Elmer/Ice developers  for technical support, input and discussions and  Martin Lüthi  ( University of Zurich ) for sharing experience and data from his previous modeling study at CG. We also would like to acknowledge  Helene Hoffmann  for providing unpublished radiocarbon dating results. We are grateful to all people that supported us for field work:  Helene Hoffmann ,  Lars Zipf ,  Michael Sabasch ,  Leo Sold ,  Martina Barandun ,  Marlene Kronenberg  and the mountain guides  Richard Biner  and  Jeremias Varela. Logistic support during field work was provided by the  Air Zermatt , the staff of the  Capanna Regina Margherita ,  Rifugi Monterosa MBG , and the  High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG). Moreover, we would like to acknowledge the  Laboratory of Hydraulics, Hydrology and Glaciology (VAW) ,  ETH Zurich , that kindly provided the DIBOSS device to perform borehole inclination measurements.
Devices for field work were also provided by the  Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bremerhaven (AWI)  and the  TS-CCEES Group at the IUP Heidelberg. Finally , a very warm thank you goes to the late  Dietmar Wagenbach  ( IUP ) for his long-standing research at  CG , for providing the conditions to start the project and his guidance in the very initial phase. Financial support for this study was provided by the  Helmholtz Climate Initiative REKLIM. Recovery and analysis of the 2013 CG ice core KCC were supported by the  Arcadia Fund of London  ( AC3450 ) and the  Helmholtz Climate Initiative REKLIM. Work on the 2005 CG ice core KCI has been funded by the  European Union  under contract  ENV4-CT97-0639  (project  ALPCLIM ) and within the project  ALP-IMP  through grant  EVK2-CT2002-00148.
Methodology We performed a database search focusing on UM studies that intersect with the ES research field. We narrowed down the list of selected papers through a sequence of filters and assessed the results according to a set of key investigation themes. These filters are depicted in Figure  1  and together with the investigation themes are explained in more detail in the following sub-sections. Later, the information found in the literature was used to outline pathways towards a holistic integrated UMUES modelling framework. We identified these pathways by investigating which types of information in UM case studies could be used to incorporate assessment of UES. Thereafter we investigated the types of UM analytical tools that were used to model with ES-related information.
Database Search We searched for literature limiting the selection of literature to journal papers that had a focus on UM-based methods allowing the incorporation of UES. We experimented with several combinations of UM and UES search strings with the aim of revealing literature that embodies the UM metaphor and assesses ES, ecosystem health or ecosystem functioning. However, ES approaches are sometimes referred to with varying and ambiguous nomenclature, hence the inclusion of alternative wording. With the emphasis on expanding the UM metaphor to encapsulate an assessment of ES, we searched for literature with UM as the subject, and refined this to the subset of UM literature that referred to ES. This subset was identified by including several alternative words related to ES based on the string used in the review of Luederitz, et al.
[6]. We used the following string to search the SCOPUS database on 19th November 2018. The SCOPUS database was used instead of other databases (such as Web of Science) because of the superior number of journals covered, as pointed out by previous research in this field, e. g. , Aghaei Chadegani, et al. [38]. (TITLE-ABS-KEY ("urban metabolism" AND ("ecosystem" OR "ecosystem servic*" OR "ecosystem functio*" OR (provisioning AND ecosyste*) OR (regulating AND ecosyste*) OR (cultural AND ecosyste*) OR (supporting AND ecosyste*) OR (habitat AND ecosyste*)))) This search yielded 120 literature items. These were limited to English language papers published in journals, following the process in Figure  1. The remaining 80 papers were split into reviews and articles. Only the full text of the 70 remaining research articles were reviewed for relevancy to the research question.
For example, supply of climate regulation is global regardless of the land use and origin of greenhouse gas emissions  [108]. However, understanding how these patterns change in relation to material-energy stocks and flows and their impact on the supply of ES requires a detailed database of historical land use maps from which those spatio-temporal dynamics may be revealed, calibrated and validated  [47, 109]. Land use and land cover maps (e. g. , Urban Atlas; Montero, et al. [110] ) can be used to estimate physical qualities (e. g. , as surface type and imperviousness, tree cover), and as proxies to spatially disaggregate material-energy stocks and flows (e. g. , construction materials in urban fabric)  [110]. In this case, data should be disaggregated to a spatial unit of measurement to capture the spatial patterns.
Another approach is to consider functional genes as community traits and to use the diversity of gene variants as trait value, as it reflects the diversity of organisms TA B L E 1 Microbial genotypic functional traits important to biogeochemical cycling, organic contaminant degradation, stress responses, antibiotic resistance, and virulence    S2. Characterization and classification of functional genes presented in these tables were realized using information available in databases such as NCBI, UniProt, or EXpasy but also based on extensive literature reviews.
This database represents the typical type of taxon-trait data required to bridge the gap between functional ecology of micro-and macroorganisms through the use of common methods previously limited to macrobial organisms. In a community-centered perspective, phenotypic traits can correspond to community-aggregated traits  (CATs, Fierer et al. , 2014) , but often correspond to estimates of community functioning sensus studies relating biodiversity and ecosystem functioning (BEF). For instance, this includes substrate use profiles (e. g. , Biolog), production of gases, degradation of compounds, or temporal stability of biological process (e. g. , biomass production, rate of compound degradation). These community-level phenotypic traits are much more difficult to predict than their taxon-level counterparts, notably as they arise from the interaction of many organisms.
| Estimating microbial functional diversity using a taxa-traits approach Here, we present an example of functional diversity estimation following a taxa-traits approach and using tools developed for macroorganisms. To this end, we used the database released by  Barberán et al. (2017) , which contains the phenotypic and environmental tolerance traits for more than 5,000 bacterial strains. From this database, we selected 298 species from the soil habitat characterized by three continuous response traits corresponding to the species' optimal growth conditions in terms of salinity, pH, and temperature. F I G U R E 1 Estimation of microbial functional diversity using a taxa-traits approach. (a) and (b) represent the position of 248 soil bacterial species in the functional space defined by three continuous response traits; growth optima in salinity, pH, and temperature.
The third and fourth sets were composed of 264 ± 5 species with similar trait values but different distribution of species across trait values. One set of communities was dominated by species with intermediate trait values (highFD-even) while the other was dominated by species with extreme trait values (highFD-uneven, dominance by species from the orange and green functional groups). We used the R package FD to estimate two different facets of functional diversity, richness, and evenness  (Laliberté, Legendre, & Shipley, 2015). Functional richness (Figure  1c ) is the amount of functional space occupied by the organisms composing the community and represents the range of traits observed in the community  (Villéger et al. , 2008). As expected, the two sets of communities with more species have a higher functional richness (highFD-even and highFD-uneven).
| Characterizing the distribution of microbial functional traits within soil communities In this last case study, we demonstrate that gene-centered approaches can be used to characterize the distribution of functional traits within soil microbial communities and more particularly along a gradient from rarity to prevalence. Here, we used the same data as for the previous case study  (Wu et al. , 2017; Yue et al. , 2015). The dataset consisted of 60 soil communities characterized using functional gene arrays  (FGA, composed of 39, 681 variants representing 194 traits). We determined the rank-abundance distribution of all the variants within communities. We used the signal intensities of variants to order them along a rare to prevalent spectrum that corresponded to ten abundance quantiles. The abundance of traits carried by variants from each quantiles can then be estimated to identify traits that are over/underrepresented along this spectrum (Figure  3 ).
, 2003; Long, 2010). One of the first thin cloud algorithms to be developed  (Shields et al. , 1989 (Shields et al. , /1990 ) was based on the ratio Red/Blue (R/B) threshold method nowadays commonly adopted in most of the algorithms processing data from sky cameras and used to distinguish clear from cloudy sky pixels. Field images were used to obtain the corresponding blue to red ratio images before a threshold was set for determining the presence of thin clouds. It was found that uniform thin clouds gave rise to a significant increase in this ratio, lending themselves to detection. In a more recent version of the same algorithm  (Shields et al. , 2013)  thin cloud detection is based on a haze-and aerosol-corrected clear-sky NIR/blue ratio image. The algorithm automatically corrects for aerosol-haze variations and hardware artefacts.
The colour camera has a Bayer mosaic used for filtering the red, blue and green wavelengths. The MATLAB built-in P. Dandini et al. :  Halo ratio from ground-based all-sky imaging function "demosaic" is applied to the raw image data to reconstruct the full colour image. The fisheye lens enables sky observation over a field of view (FOV) as large as 187 • 59 along the ENE-WSW direction and as large as 142 • 38 along SSE-NNW. The FOV of the daytime camera along ENE-WSW is slightly larger, 189 • 53. The difference is due to the different alignment of the lens relative to the sensor. This is also reflected in the position of the true zenith (x 0 , y 0 ) with respect to the centre of the camera plane, which for both cameras does not coincide with the true zenith. An offset of 14 pixels in the x direction and 12 pixels in the y direction that corresponds to about 4 and 3.
(2011) , every 5 min, over data sampled with 1 s resolution and for time intervals ranging between 20 and 60 s. The intervals were chosen as the time range over which the DFA function was relatively stable and the slope of the function (the FC) was as sensitive as possible to the presence of clouds. The DFA algorithm was applied to 1 year of data, and the corresponding FC time series was averaged over clear-sky periods to set the DFA threshold. This was obtained after manual cloud screening of images associated with minima of the fluctuation coefficient. Furthermore, we wish to point out that like in  Brocard et al. (2011)  the initial analysis step of cumulative summation (integration) of the time series was not carried out. We note that cumulative summation of a self-affine series shifts the FC by +1  (Heneghan and McDarby, 2000) , so the threshold applied here would have a value close to 1 if a standard DFA procedure was followed.
Consequently, while not computationally demanding and relatively easy to implement, this method allows the range of application of all-sky imaging to be broadened beyond the more qualitative recording of cloud fields and optical displays associated with cirrus. The cloud classification method, on the other hand, is original in that it relies on a non-fixed, non-location-specific and easy-to-model temperature threshold. The combined use of these two methods allows relatively inexpensive halo observations and the retrieval of information pertaining to ice particle size and texture. If implemented at multiple locations, the methods can provide a useful dataset for improving the understanding of cirrus composition. Data availability. The all-sky camera images are archived on the observatory website at http://observatory.
Acknowledgements. The services of the  Natural Environment Research Council (NERC) British Isles continuous GNSS Facility (BIGF) , http://www. bigf. ac. uk (last access: 4 January 2019), in providing archived GNSS products to this study, are gratefully acknowledged. We thank  Paul Kaye  for his contribution to designing the occulting disc and  Evelyn Hesse  ( University of Hertfordshire ) and  Anthony Baran  ( Met Office ) for their valuable advice. Zbigniew Ulanowski  acknowledges support from the  Natural Environment Research Council  grant  NE/I020067/1. Edited by: Bernhard Mayer Reviewed by: two anonymous referees.
Functional traits can be valuable pieces of information for aquaculture research and management. Although fish traits have been the focus of an abundant research, trait datasets for these organisms are difficult to access and often unpractical to achieve meta-analyses without a time-consuming extensive review. Already available large-scale compilations include trait information for many fish species but not as detailed as required for aquaculture purpose. Here, we introduce the TOFF (i. e. Traits OF Fish), a database focusing on fish functional traits that aims at bringing together behavioral, morphological, phenological, and physiological traits always coupled to environmental measurement context into a single open-source access repository. TOFF hosts data from published field and experimental studies.
Therefore, functional trait-based approach can limit time and money-consuming trial-and-error bioassays to assess alternative aquaculture development scenarios. This places a premium on the compilation of species trait datasets with broad taxonomic coverage in species groups significant for aquaculture, such as fishes, to foster applied developments through big data-based meta-analyses. For more than a century, an abundant research has extensively studied the fish traits for a variety of purposes. This means that sufficient data might well already exist for addressing alternative development hypotheses and meta-analyses about fish aquaculture. However, an overview of broad-scale insights is poorly available because useful datasets are scattered over several decades of literature in many languages.
Some trait compilations have already been proposed (e. g. ,  [15] [16] [17] [18]  ) but most of them focus on particular set of traits to address specific questions, fail to ensure homogeneity of data (e. g. , no standardized definitions of traits making comparison from different sources doubtful), gather information for a limited geographic area/taxonomic range, and/or has restricted availability. Although original citations are provided, the largest available comprehensive compilations of fish traits (i. e. FishBase [www. fishbase. org] or  19  ) tend to provide only a synthesized mixture of original and meta-analysis datasets. Furthermore, the lack of important contextual information about measurement environmental context or levels of variation and replication for trait data limits such compilations.
Yet, functional traits are environment-dependent  20  and display variability at the interpopulational level  21. Therefore, a database hosting detailed information about functional traits and their environmental measurement context, which are required for aquaculture purpose, is still lacking. Here, we introduce TOFF (i. e. Traits OF Fish) database, the first curated collaborative database of functional trait information for fish species significant for aquaculture (i. e. seawater, brackish, and freshwater species). The aims of the database are: (i) to gather disparate information on fish traits associated with information about measurement environmental context, (ii) to provide unrestricted open-source access to fish trait data, and (iii) to engage the fish research community in the collection and quality control of trait datasets.
Methods TOFF focuses on functional traits of fish (sensu all taxa included in Myxini [hagfishes], Cephalaspidomorphi [lampreys], Holocephali [chimaeras], Elasmobranchii [sharks and rays], Sarcopterygii [lobe-finned fishes], and Actinopterygii [ray-finned fishes]), the most taxonomically diversified group among chordates with more than 34,000 species (according to FishBase [www. fishbase. org]). The database can host various types of data from multiple sources of literature (i. e. peer-reviewed articles, theses, monographs, and books). It has been designed to contain "records" that are information about BMPP traits for a fish species, at a specified developmental stage (i. e. larvae, juvenile, or adult), sex, and ploidy level in a characterized environment from a scientific source. This means that a study considering, for instance, different environments and/or developmental stages, sexes, and ploidy level is encoded as several records in the database.
Database description. Considering the heterogeneous formats and contents of data referring to BMPP traits and environment measurements, we develop a flexible SQL relational database using EAV (entity -attribute -value) model coupled with controlled vocabularies. These latter ones are semantic tools, which consist of a list of terms, their definitions, and some of their properties such as their unit, their range (e. g. , from 0 to 14 for pH), their spelling into drop-down menu (e. g. , for categorical variables), or their format (see detailed thesaurus in  22  ). Automatic controls are implemented in the database following the format, the spelling, and/or the range following thesaurus guidelines. TOFF uses postgreSQL as database management system. Each record in the database is unique, identifiable, checked by scientific expert board, and traceable.
The database is hosted by the data center of the Institut national de la recherche agronomique (INRA) and baked up daily. The relational database schema contains five core modules (Fig. 1 , see detailed data model in  22  ). First, the Reference module stores bibliographic metadata (e. g. , authorship, publication type, publication year). Second, the Trait module integrates functional traits included in the database. These traits are grouped into four classes (i. e. behavioral, morphological, phenological, and physiological traits) and defined following the thesaurus  22. Third, the Measure module includes each measure (i. e. attribute of a trait) of a particular trait. It is linked to the Trait module. The Measure module includes also information about the effective size (i. e. notifying on how many individuals is based the trait measure), the ploidy level (e.
Data acquisition. All records in the TOFF database are extracted from (i) published resources including peer-reviewed articles, theses, monographs, and books or (ii) experimental studies (unpublished data). In the former case, the original source is always specified and must be provided by contributors. This allows performing meta-analyses citing all relevant references and integrating some or all type of published resources. Three types of data are recorded: continuous numerical values, discrete, or categorical data. Measurement value types include mean, standard deviation, median, maximum, minimum, and model derived. Continuous data are typically means extracted from tables or figures unless raw data are available. Aggregate values (e. g. , means and medians) are always accompanied by the number of replicates and a measure of dispersion (e.
g. , standard deviation). The dataset here released have broad taxonomic range. However, some large data gaps exist because of the time-consuming review process required to feed the database. This places a premium on the swift development of a contributor community for TOFF to increase the dataset included in the database. In order to facilitate such a development, we propose an optimized importation tool from csv files to integrate large dataset in TOFF (see TOFF Assistant website: http://toff-project. univ-lorraine. fr).