dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we compiled and evaluated a global diet dataset for terrestrial mammals (referred to as "MammalDIET" for general information see Table  1 ). We first digitized diet information from two comprehensive, global and cladewide data sources that provide a relatively standardized way of presenting mammalian diet information  (Nowak 1999; IUCN 2013 ). We then quantified the consistency of data entry by multiple data recorders and developed an extrapolation procedure to fill-in missing diet information at the species level. Extrapolation was performed by using available diet knowledge from other species or other taxonomic levels (genus, family). We then validated the extrapolation procedure (both internally and externally) to identify the most reliable diet categories for classifying mammal species into trophic levels and dietary guilds.
Finally, the frequency of different trophic levels and dietary guilds within mammalian families and orders was quantified and their species richness and proportion were mapped at a global scale. With the developed methodological framework (summarized as a flowchart in Fig. 2 ), we estimated diet preferences for nearly all terrestrial mammal species worldwide. We further provide MammalDIET as a freely available resource to enable macroecological and macroevolutionary analyses, and we encourage researchers to use, test, apply, and refine this dataset in the future.
Availability MammalDIET is available from the Dryad Digital Repository: http://doi. org/10. 5061/dryad. 6cd0v synonyms where names differed between sources. We excluded marine families, but included all terrestrial mammal species (n = 5364). We converted written text descriptions (for examples see Table  2 ) of diet preferences from the two literature sources into ordinal data (ranks 1-3). In cases where the text did not allow inferring the relative importance of diet categories, we entered rank 1 assuming that these food items were equally important. In a few cases, the IUCN (2013) data source described species as, for example, carnivorous, herbivorous, frugivorous, omnivorous for which we recorded rank 1 in the corresponding diet categories. A zero (rank 0) was assigned if a specific diet category was not recorded in the literature for a given taxon.
Moreover, we took a conservative approach and recorded diet information only at the specific taxonomic level of the original data source, that is, at species, genus, or family level. The majority of diet information from  Nowak (1999)  was available at the genus level, and hence, we digitized this information at the genus level, not at the species level, even if species within a genus are likely to have the same diets. In contrast, the IUCN (2013) data were almost exclusively available as species-level information. For digitizing the data ("digitalization" in Fig. 2 ), a total of sixteen diet categories at four hierarchical levels were distinguished (Fig. 3 ). At the first and coarsest level, we distinguished between "Animal" and "Plant. " At the second level, the animal category was subdivided into "Vertebrate" and "Invertebrate.
" At the third level, the plant category was subdivided into "Seed", "Fruit", "Nec- Occasionally, sometimes, small amounts, supplemented by, a few, rarely, opportunistically tar", "Root", "Leaf", and "Other" material, and the vertebrate category was subdivided into "Mammal", "Bird", "Herptile" (amphibians and reptiles), and "Fish". At the fourth and finest level, we subdivided the leaf category into leaves from woody plants ("Woody") and leaves from herbaceous plants ("Herbaceous"). This was carried out to allow the division of mammalian herbivores into browsers and grazers. For all data entry, we recorded diet preferences down to the finest diet categories possible. At the beginning of the data digitalization process, we tested how well diet descriptions from the data sources could be converted into diet ranks in MammalDIET.
To assure the consistency of data entry by multiple data recorders (all authors except J. -C. S. ), we randomly selected 20 mammal species from  Nowak (1999)  before compiling the data and each data recorder then ranked diet descriptions from the source for the same sample species ("calibration" in right-hand side of Fig. 2 ). Discrepancies in data entries between data recorders were subsequently discussed among all persons to minimize errors of the digitalization process, that is, when transferring written diet descriptions from the sources into an ordinal scale in MammalDIET. Such a calibration step was used to standardize the digitizing of data by multiple recorders. After the data from  Nowak (1999)  had been assembled, an additional test ("quality check" in righthand side of Fig. 2 ) was performed based on 120 randomly selected species out of those species for which diet data from  Nowak (1999)  were available at the species level (n = 682).
For the other species, data extrapolation was performed hierarchically. First, diet data were filled from the genus level (FillCode = 1), then from other species within the same genus (FillCode = 2. 1 or 2. 2) and finally from the family level (FillCode = 3). We distinguished two ways of data filling from other species within the same genus. First, if only one species in the genus had data, we applied this information to our missing species (FillCode = 2. 1). Second, if more than one species in the genus had data, we assigned the diet information for each category to the missing species if all species had the same information in that category (FillCode = 2. 2), that is, we only extrapolated information that was consistent among congeneric species. Otherwise data were assigned as not available ("NA").
Internal validation of extrapolation To investigate the robustness of the extrapolation procedure, we applied two validation procedures ("validation" in Fig. 2 ): first an internal validation (using the specieslevel data from the compiled dataset) and second an external validation (using an independent data source, see below). For the internal validation, we evaluated how well each of the species with species-level diet information in the compiled dataset (i. e. , FillCode = 0; n = 2033 species)   S1. would be filled if no diet data were available. We used a jack-knife approach where diet ranks were removed from one focal species at a time and then filled by the same extrapolation procedure as described above. This predicted the diet ranks for the focal species as if there was no diet information available for that species.
External validation of extrapolation We used an external validation to test the accuracy of the extrapolated species-level diet data in MammalDIET relative to diet data extracted from an additional, independent data source (see validation in Fig. 2 ). For this additional data source ("external validation dataset"), we chose the species accounts from the new edition of Mammals of Africa  (Butynski et al. 2013; Happold 2013; Happold and Happold 2013; Kingdon and Hoffmann 2013a,b; Kingdon et al. 2013) , a series of six volumes describing in detail every currently recognized species of terrestrial mammal in Africa. Although having a regional focus (African continent), this compilation of books is the most comprehensive, up-to-date species-level data source that is currently available for mammals in a specific biogeographic region.
From the full list of all species with extrapolated diet information in our dataset (n = 3329), we first selected those occurring in Africa (n = 611) and then randomly selected species from this list to subsequently enter diet information from the Mammals of Africa. To aim for a reasonable sample size of species across different diet categories, we stratified the random selection by choosing 30 random species (if available) for each of the sixteen diet categories. Several species were selected more than once and we removed these duplicates and thus ended up with a total of 289 randomly selected species. For each of these species, we checked the diet information in Mammals of Africa and entered species-level diet information in the same way as for MammalDIET (if available). For each of the 16 diet categories (cf.
Fig. 3 ), we then compared how often the diet information from the external validation dataset (Mammals of Africa) was consistent with the extrapolated species-level knowledge in MammalDIET. We report the percentage of correctly extrapolated diet ranks (separately for rank 1 only, and for rank 1 and 2 combined) for the 16 diet categories. We performed this validation for all species in the external validation dataset as well as separately for mammal orders with ≥15 species.
Spatial visualization To illustrate potential applications of the presented data, we combined the trophic level and dietary guild classification with data on the global distribution of mammals. We used the global species distribution maps for terrestrial mammal species from IUCN (2013). We converted the polygon range maps to rasters on a Behrmann cylindrical equal-area projection and extracted species occurrences for grid cells at a resolution of 2°equivalents (~220 km). We chose 2°equivalents over 1°equivalents, but we note that statistical analyses with range maps at these two spatial resolutions usually give similar results (e. g. ,  Hurlbert and Jetz 2007; Kissling et al. 2012). The data handling and extraction were similar to the procedure described by  Sandom et al. (2013). We mapped the global distribution of species richness and proportions for each trophic level and dietary guild, excluding Antarctica and grid cells with <50% land area.
Digitalization of data Of the 5364 terrestrial mammal species, a total of 2033 species (38% of all terrestrial mammal species) had species-level information available from the two global data sources. Of those, 682 species were entered from  Nowak (1999)  and an additional 1351 species from IUCN (2013). Furthermore, information on diet preferences were also available for many genera (n = 453) and families (n = 32). These genus and family diet data were only used for the extrapolation procedure. The calibration step before the data digitalization confirmed that minimizing discrepancies between multiple data recorders can be important when transferring written text descriptions into semi-quantitative ranks. The subsequent additional quality check of 120 randomly selected species revealed a relatively low error rate due to data entry via multiple data recorders.
The former of these two species seems to be phylogenetically nested within the genus Coendou  (Voss et al. 2013 ) and can therefore be considered as herbivorous, whereas the latter is extinct  (IUCN 2013 ) and dental morphology suggests a predominantly herbaceous diet  (Angelone 2005). Note that we did not enter this additional information into MammalDIET as it was not available from the two original data sources. In total, the original data together with the extrapolation procedure provided species-level data on diet preferences for 99. 9% of the world's terrestrial mammals (n = 5362 species). Across all terrestrial mammal families, the percentage of species with different filling codes varied widely (Fig. 4A ). However, species-level diet information was typically available for half of the species within a given family (median: 54%).
Internal validation of extrapolation The internal validation with the compiled dataset showed that the extrapolation procedure performed best for primary food items (rank 1, Fig. 4 ), especially for coarse diet categories such as "Animal" and "Plant" (Table  4 ). More generally, diet categories at high hierarchical levels ("Animal", "Plant", "Vertebrate", and "Invertebrate") were on average better predicted than those at low hierarchical levels (all other categories) (Fig. 4 ). However, a few diet categories at low hierarchical levels ("Mammal", "Seed", "Fruit", and "Leaf") also showed good validation results for rank 1 (Table  4 ), whereas the "Vertebrate" category had the lowest predictive potential among the higher hierarchical levels (Table  4 ). The prediction of the absence of a diet category (rank 0) was generally very good (Fig.
External validation of extrapolation Of 289 African species in the external validation dataset, 163 species (56%) had species-level diet information available from the six volumes of Mammals of Africa. The number of species for validating the sixteen different diet categories was generally good (>10 species) although two diet categories ("Fish", "Nectar") had insufficient sample sizes (3 and 0 species, respectively). For diet categories with sufficient sample sizes, the external validation showed that three diet categories at high hierarchical levels ("Animal", "Plant", and "Invertebrate") as well as four diet categories at low hierarchical levels ("Mammal", "Seed", "Fruit", "Leaf") correctly predicted the diet ranks in ≥60% of the cases (Fig. 4C ). The same diet categories were also identified with good validation scores by the internal validation.
High hierarchical levels include the diet categories "Animal", "Plant", "Vertebrate", and "Invertebrate", whereas the low hierarchical levels include all other diet categories. Information on ranks 1-3 is provided in Table  2. The "0″ indicates that a diet category was not used (i. e. , assumed absence). In (C), extrapolated diet data are validated independently with an external validation dataset (Mammals of Africa, see text for details). The percentage of correctly predicted diet ranks is given for each of the sixteen diet categories for rank 1 data only (gray bars) and for rank 1 and 2 data combined (white bars). Numbers below diet categories give the sample size (number of species) for each validation. Boxes in (A) and (B) represent the interquartile range (IQR), horizontal lines within the boxes represent medians, whiskers extend to 1.
5 times the IQR, and outliers are plotted as dots. To explore taxonomic variation in extrapolating diet ranks, we examined the results from the external validation separately for each of five mammal orders with ≥15 species (Figs  5  and 6 ). This revealed interesting differences in the potential to predict diet adaptations both within and among clades. Two orders (Rodentia and Carnivora) showed a broad range of diet categories, but their specialization on plants and animals differed. Rodents (Rodentia), being predominantly herbivorous and insectivorous and representing the most species-rich order in the external validation dataset (as well as globally, Table  3 ), showed a 100% prediction accuracy for the diet category "Plant", but a mixed picture with varying percentages of correctly predicted diet ranks for other categories (Fig.
Classification of trophic levels and dietary guilds Based on the internal and external validation results above, two classification procedures were applied (for Table  3. Summary information across mammal orders of how extrapolation of diet preferences was performed (FillCode = 0, 1, 2. 1, 2. 2, 3). Diet information was available for 2033 species at the species level from the original data sources (FillCode = 0). For the other species, diet data were first filled from the genus level (FillCode = 1, n = 2556 species), then from one other species (FillCode = 2. 1, n = 266 species) or from more than one species within the same genus (FillCode = 2. 2, n = 71 species), and finally from the family level (FillCode = 3, n = 436 species). See text for details. 47 ) 266 (  5 ) 71 (  1 ) 436  (8)  details see Table  5 ).
These dietary guilds were not mutually exclu-sive because a species could be classified into more than one dietary guild (e. g. , granivore, frugivore) if it had a rank 1 in these diet categories ("Seed", "Fruit"). A detailed overview of the two classifications is provided in Table  5. The dietary guild assignment for each species is also provided with the dataset (Appendix Table  S1 , dataset available from the Dryad Digital Repository: http:// doi. org/10. 5061/dryad. 6cd0v). The percentage of species within trophic levels and dietary guilds varied considerably among mammal orders (Table  6 ). For instance, the largest proportions of carnivorous species (as defined in Table  5 ) were found in the mammal orders Dasyuromorphia (a group of Australian marsupials), Eulipotyphla (such as shrews), and Afrosoricida (tenrecs, otter-shrews and golden-moles).
Discussion By digitizing, extrapolating, and validating diet preferences of terrestrial mammals worldwide, we compiled a comprehensive and unique, cladewide trait dataset (Mam-malDIET) relevant for macroecological and macroevolutionary analyses. In contrast to previous datasets that have been made available to the public  (Jones et al. 2009; Price et al. 2012; Cantalapiedra et al. 2014 ), MammalDI-ET allows a finer dietary guild classification and a broader taxonomic coverage. This was achieved by a combination of original and extrapolated data, thus providing specieslevel diet estimates for >99% of all terrestrial mammals. Results from the internal and external validation steps confirmed the use of several diet categories as reliable information for subsequent classification of species into trophic levels and dietary guilds.
The methodological approach used here (summarized in Fig. 2 ) could also be applied more widely when constructing global databases of species-specific traits. Digitalization of available trait data represents an important step in the compilation of macroecological trait datasets. During this process, errors can occur, for example, when written text descriptions are converted into (semi)quantitative data. We used a calibration step with 20 randomly selected species before entering the data to ensure that diet information was digitized in the most consistent way among multiple data recorders. Furthermore, we tested the error rate due to data entry via multiple data recorders using 120 randomly selected species. This revealed that converting written diet descriptions from textbooks into (semi)quantitative diet ranks was not particularly prone to errors.
We found that most diet ranks were entered in the same way by multiple recorders, with an accuracy of almost 95%. Nevertheless, some diet categories such as other plant material ("Other") had a lower success rate (86%) which demonstrates a larger uncertainty in the assigned importance score for such unspecific categories. We emphasize that initial calibrations and subsequent data quality tests were valuable steps to avoid discrepancies in data entries and to maintain the consistency of data entry by multiple data recorders. Other authors of mammalian diet datasets (e. g. ,  Price et al. 2012 ) also verbally report such cross-validations of scoring by multiple recorders although quantitative assessments Correctly predicted diet rank (%) are usually not provided. We therefore suggest that explicit guidelines for how to convert diet descriptions into ranked importance scores are needed when many recorders are involved in building up macroecological trait datasets  (Jones et al.
2009 ). To accommodate the lack of species-level traits in sparse datasets, an extrapolation or prediction of missing trait data based on non-missing entries from other taxonomic or phylogenetic levels might often be the only way to compile macroecological trait datasets with a global coverage  (Shan et al. 2012). Our hierarchical extrapola-tion procedure allowed to fill-in gaps of diet information when species-level information was not available from the two original data sources. For some taxonomic groups (e. g. , Rodentia, Eulipotyphla), the missing data reflect the limited diet knowledge at the species level. This became evident in the external validation which showed that for many extrapolated species additional species-level diet data were not available, even not from the most comprehensive regional data sources  (Butynski et al.
2013; Happold 2013; Happold and Happold 2013; Kingdon and Hoffmann 2013a,b; Kingdon et al. 2013). For instance,  for Eulipotyphla (here mostly represented by shrews of the genus Crocidura in the family Soricidae) and Rodentia (various mice genera in the family Muridae), the external validation dataset based on the Mammals of Africa did not provide species-level diet information for 65% and 49% of the species, respectively. Nevertheless, we acknowledge that more species-level diet data could be extracted from additional data sources for some of the species which currently have extrapolated diets in Mam-malDIET. In such cases, MammalDIET could serve as a baseline source for adding additional data and the data coverage for such species could then be improved. Extrapolation will be most reliable if taxa show a high level of phylogenetic conservatism in their diets.
Compared with previously published datasets, MammalDIET represents an improved classification of dietary guilds in terrestrial mammals worldwide because the diet data is more detailed and provided in a quantitative format that facilitates customized diet reclassifications. For instance,  Price et al. (2012)  assembled coarse mammalian diet data and classified species into three trophic levels (carnivores, omnivores, herbivores), covering only approximately one-third of the mammals (n = 1530 species). Jones et al. (2009)  recorded eight diet categories and classified mammals into three trophic levels (carnivores, omnivores, herbivores), but only for around 40% of the species. Jetz et al. (2009)  compiled diet data for >90% of the mammal species, but only distinguished two trophic levels (primary and secondary consumers), and the data were not made publicly available.
MammalDIET provides data for 16 diet categories that can be combined in many ways to generate any kind of customized dietary guilds. This enables a much more refined classification of dietary guilds than previously possible, and researchers are free to define diet guilds tailored to the question they are investigating. Our validation results further support previously applied classifications (e. g. ,  Sandom et al. 2013)  and suggest that results using 2-3 trophic levels based on similar data  (Jetz et al. 2009; Price et al. 2012 ) should be relatively robust and reliable. Until now, no global dataset on detailed mammalian diets and high species coverage has been publicly available. MammalDIET therefore constitutes a new global resource to enable macroecological and macroevolutionary analyses in mammals.
Conclusions The compilation of macroecological trait datasets such as MammalDIET is challenging and requires several methodological steps, from digitizing accessible information to extrapolating missing data and validating extrapolation procedures. The approach illustrated here provides an example to fill-in data gaps in mammalian trait information and could be applicable more widely to other traits and taxa. Due to large knowledge gaps on traits of species-rich clades, we suggest that a comprehensive effort into the compilation and prediction of traits is needed to significantly advance macroecological and macroevolutionary research. Fundamental to this effort will be a deeper understanding of phylogenetic conservatism in traits, that is, when it matters and how it varies across taxonomic and phylogenetic scales.
3). High hierarchical levels include the diet categories "Animal", "Plant", "Vertebrate", and "Invertebrate", whereas the low hierarchical levels include all other diet categories. Information on ranks 1-3 is provided in Table2. The "0″ indicates that a diet category was not used (i. e. , assumed absence). In (C), extrapolated diet data are validated independently with an external validation dataset (Mammals of Africa, see text for details). The percentage of correctly predicted diet ranks is given for each of the sixteen diet categories for rank 1 data only (gray bars) and for rank 1 and 2 data combined (white bars). Numbers below diet categories give the sample size (number of species) for each validation. Boxes in (A) and (B) represent the interquartile range (IQR), horizontal lines within the boxes represent medians, whiskers extend to 1.