dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors are grateful to  Denise Candeias  for help revising the manuscript. We thank the  Montpellier RIO Imaging (MRI)  platform and  Marc Lartaud  for technical advice. We acknowledge the contribution of SCIMI platform ( Dr. Aldebert Delphine  and  Touquet Bastien ) from the research unit LAPM, UNR CNRS 5163  UJF, Grenoble, France  for their help in image acquisition with T. gondii.
Notice that the input data could include not only sparse measurements obtained with field surveys, but also maps obtained from aerial or satellite imaging. Notwithstanding the excellent works proposed by different research groups, as shown by the above, incomplete list of references (see also  Gudmundsson, 2011 , for a review), IP theory, which is well developed in several areas of science and geophysics (see, e. g. Parker, 1994; Tarantola, 2004; Menke, 2012) , has not yet become popular in glaciological sciences. This remark is supported by a simple statistical analysis, whose results are shown in table 1 and which was performed with the Scopus data base. The number of papers with keywords related to inverse problems ("invers*", "model calibration", "parameter identification") and keywords related to geophysics ("geophys*"), glaciology ("glaci*") and (surface or sub-surface) hydrology ("hydro*") have been extracted from the subject areas "Earth and planetary sciences", "Environmental Science", "Physics and astronomy".
The paradigmatic example and definition of the forward problem The conceptual framework is introduced with a paradigmatic example based on the application of the model developed by  Bueler (2014)  at the University of Alaska (Fairbanks AK, USA) and implemented in the Matlab ® code siageneral. m. The model is based on a finite-difference discretization of the ice-sheet equation under the SIA and the hypothesis of constant and uniform temperature, that is  (Bueler et al. , 2005) : ∂ t H = M + ∇ • (D∇h), ( 1 ) where H is the ice thickness, t is time, h is the ice surface elevation, M is the surface mass balance and D is the diffusivity. The model does not consider ice-shelves, so that h = b+H, where b is the bed elevation. D is given by D = 2EA(ρg) n H n+2 n + 2 |∇h| n-1 , (2) where A and n are the flow parameters and E the enhancement factor in Glen's law, which is the constitutive relation assumed between strain rate ε and deviatoric stress τ : εij = EAτ n-1 e τ ij , (3) where τ e is the effective deviatoric stress.
The model is applied to the Antarctic ice-sheet: the bed elevation and the initial ice thickness are taken from the dataset ALBMAP v1 (Le  Brocq et al. , 2010)  and it is assumed that n = 3 and A = 10 -16 Pa -3 a -1. The latter value corresponds to the reference value used for some experiments of model intercomparison: EISMINT I  (Huybrechts et al. , 1996)  and ISMIP-HOM  (Pattyn et al. , 2008 ). In the paradigmatic example, it is assumed that M is constant in space and time. Of course these are very strong approximations, but they are useful to have a test to introduce some concepts and to show some results in a very simple way. A reference solution h (ref) is generated by running the model from the present-day geometry for a time period of 20 ka with the reference parameters M (ref) = 0.
3 m a -1 and E (ref) = 3. In real-world applications, acquired data should include information about the geometry of the domain, the positions of the measurement points, the measured values of physical quantities (e. g. ice-sheet-surface height, temperature at the surface and in few boreholes, ice-sheet velocity at the surface, ice accumulation rate at some monitoring stations on the surface, etc. ). All these data are collected in an array d. For the paradigmatic example, d contains the synthetic data corresponding to the nodal values of h (ref). The input model parameters, included those that describe the geometry of the discretization grid (e. g. the spacing of the grid), are contained in the array p. Some of these parameters are fixed before the application of the model and can be grouped in a "sub-array" p (fix) , which depends on the data, i.
It is also very important to stress that a clear distinction must be done among problems with the intrinsic properties of the IP and the effects of the solution method. Figure  3  shows a very simple example. On top of the graph of O the minimization paths followed by the application of the Matlab ® function fminunc, based on the quasi-Newton algorithm, with two different initializations are shown. Information listed in Table  2  show that starting from different couples of parameters values might yield different "solutions". In fact, Fig. 2  shows that the reference values lie in a "valley" characterised by a weak slope, which makes difficult the identification of the absolute minimum with a gradient-based approach. However, notice that the difference between the values of the O functions at those points are very small and even about one third of the SD of the error added to the data.
Why Fauna Europaea? The European Commission has published the Community Biodiversity Strategy to provide a framework for the development of Community policies and instruments in order to comply with the Convention on Biological Diversity. With regard to biodiversity in Europe, both science and policies depend on a knowledge of its components. The assessment of biodiversity, monitoring of changes, sustainable exploitation of biodiversity, and much legislative work depend upon a validated overview of taxonomic biodiversity. The Strategy recognises the incomplete state of knowledge at all levels concerning biodiversity, which is a constraint on the successful implementation of the Convention. Fauna Europaea contributes to the European Community Biodiversity Strategy by supporting one of the main themes of the Strategy: to identify and catalogue the components of European biodiversity into a single database to serve as a basic tool for science and conservation policies.
• Species names are binomial (a combination of a generic and a specific name). In zoology (after the first description) combinations are not formalised by the code of nomenclature. Also synonymy is not primary linked to the combination, but to the respected species-group, genus-group or higher rank names. Therefore also in Fauna Europaea taxonomic records are name based, meaning split into generic and specific names, and into accepted names and synonymous names (i. e. different names used for the same taxon). This is also reflected in the database model (see below).
Taxonomic criteria The taxonomic scope has proved to be by far the most controversial part of this project. What satisfy the entering of a species name into Fauna Europaea database? Obviously, an "accepted species" should be living, multi-cellular, non-marine animal species with a documented occurrence in Europe. However, there is more to it. An "accepted species" in a Fauna Europaea context should also be scientifically named and described according to the regulations of the International Code of Zoological Nomenclature. Further, if there exist two or more synonymous names for the same species, it is the oldest valid name that should be used. Even by these additional criteria it is unavoidable that specialists have to face difficult decisions, which are bat est, made on a mixture of common sense and tradition.
Unfortunately, the specialists often feel disinclined to categorize a nominal species as a nomen dubium, even if the name has remained obscure for many decades. One major reason could be that the FaEu database cannot presently accommodate this "third" category of names that neither belongs to the accepted names or synonymous names categories. One aspect of the future updating of the Fauna Europaea database will be the removal of any "surviving" nomina dubia from the lists of accepted species.
Spelling of species names in Lepidoptera It has become practice among Lepidopterologists to use the original spellings of the species names and not to follow Article 31. 2 and 34. 2 of the International Code of Zoological Nomenclature  (Sommerer 2002) , which requires adjectival species names to be in agreement with the gender of the genus name. For reasons of stability and interoperability with major on-line databases (Such as the Species 2000 Global Lepidoptera name database) we have chosen to show in Fauna Europaea primarily the original spelling of the specific names of the Lepidoptera. For several groups we also add (ed) as an alternative the spelling in agreement with the gender of the genus, but only for 'real' Latin or Greek adjectives. This means that for names with 'invented endings' -ella,ana, -ata, -dactyla, we always only follow original spelling.
Taxonomic framework During the project phase of Fauna Europaea, the Zoological Museum of Copenhagen collated the individual data sets for Fauna Europaea, which were sent to the Zoological Museum Amsterdam for merging into the integrated database. Member institutes of the project are involved by providing taxonomic expertise and information and expert networks taking care about data collation. A selected small network of (67) group coordinators keeps contact with the large number of around 500 experts throughout Europe to capture data files on parts of the various taxonomic groups. Every taxonomic group is covered by at least one group coordinator. Group coordinators are responsible for the supervision and integrated input of both taxonomic and distributional data. The formal responsibility of collating and delivering the data of relevant families has resided with the Taxonomic Specialists.
Validation Framework & Quality Control Fauna Europaea data are unique in a sense that they are fully expert based, meaning that the database is build from scratch by leading experts, critically reviewing and evaluating all available information  (collections, observations, research, etc. ) , not by simply merging data from available resources like faunas, monographs, checklists or collection data. For distributional details local checklists are only used selectively (when scrutinised). Furthermore, all Fauna Europaea data sets are intensively reviewed and scrutinised. Five basic procedures to check and improve the quality of the data have been effected: (1) During the project phase of Fauna Europaea, the National Museum of Natural History in Paris was in charge for validating the delivered data sets by means of cross-checking and reviewing with existing national, regional and local resources (checklists and literature), following the criteria on data standards, data semantics, data accuracy, data reliability, and inclusiveness of data as developed for Fauna Europaea.
IT services and data management In Fauna Europaea all data editing is done on a central system by the experts themselves. By evaluating team structure and life-cycle procedures (data-entry, validation, updating, etc. ), clear definitions of roles of users and user-groups, according to the taxonomic framework were established, including ownership, and read and write privileges, and their status changes during the project data-flow (Fig. 4 ). In addition, guidelines on common data exchange formats and codes have been issued. To facilitate data transfer and data import within the Fauna Europaea database on-line and off-line data-entry routines have been build (Fig. 5 ), including: (1) An on-line Web interface data-entry tool (step-by-step through web-interfaces). (2) An off-line data import tool by use of a pre-defined spreadsheet file; including validation checks, and inventive data import and export routines.
For details on the data-entry tool functional interfaces, please consider the respective manuals. Based on the functional and technical requirements a database model has been developed (Fig. 6 ), including components to manage taxonomic data, distributional details, references and user information. Fauna Europaea follows a typical name-based data model proceeding from a 'Taxon table' including names as single entities (uninomials/monomials), defining the classification, name combinations and synonymy as recursive relationships between names. This model is able to handle the complex issues of zoological nomenclature and synonymy. The final Fauna Europaea server-infrastructure included an Oracle RDBMS transactionserver interconnected to a set of tools for data entry and data transfer, establishing an advanced virtual environment for data import and management, which well performed for more then 10 years and an Oracle RDBMS production-server linked to the Fauna Europaea web portal.
Results Fauna Europaea results in a unique overview of the state of art with respect to our understanding of the taxonomy and occurrence of European species and serves as a clearing house to identify taxonomic knowledge and expertise, including potential gaps. As a taxonomic data standard resource, Fauna Europaea provides a gateway serving the integration and sharing of European biodiversity data, supporting major biodiversity informatics initiatives, such as LifeWatch, EU BON, and GBIF, as well as serving many other stakeholders, such as national biodiversity portals, nature conservation agencies and biodiversity managers.
Fauna Europaea main outcomes The first release of the Fauna Europaea index was formally presented at the Fauna Europaea final meeting at the 27 of September 2004 in Paris. In the following years new releases have been published at around a yearly sequence. The Fauna Europaea expert network includes 67 Group Coordinators and up to 500 specialists. Since 2004, several expert replacements took place, especially substituting retired or deceased specialists (see Table  2  on Group Coordinator changes). In general the expert network remained rather stable for the last 15 years, although the actual participation and policy to contribute to new updates varied significantly within and between the different taxonomic groups and data sectors. Some group coordinators remain very active and delivered new data sets for each release, others were more passive, but allowed taxonomic specialists to contribute individually, and for some groups neither the group coordinators were active nor the taxonomic specialists could contribute.
During the last updating period around one third of the Fauna Europaea experts could be considered as 'active'. The geographic details of the Fauna Europaea data have been subject of various analyses within Europe (e. g. Essl et al. 2013 , Fontaine et al. 2012)  or with adjacent areas  (Baron 2008). In general species richness shows a positive correlation with country size and lower altitude, endemicity being substantially higher in countries with island archipelagos (Fig. 8 ). Source data can be found at Suppl. materials 7, 8. Fauna Europaea web-portal interface (faunaeur. org). Figure  13. PESI target species lists search interface. Around 2005, the relevance of applying unique and stable species identifiers, to secure long-term internal consistency and optimise external interoperability was recognised in the biodiversity informatics community  (Hussey et al.
2008). Following the zoological context of the Fauna Europaea database, species identifiers are released at a specific level, maintaining species-group names as rigid identifiers, not species names (combinations). A positive result of this feature is that species names always resolve towards their nominal taxa (the only fixed point in zoological nomenclature), a disadvantage is that new combinations, such as changed genus assignments of the same nominal species, have similar taxon IDs between different versions of Fauna Europaea (see also Discussion). To make the Fauna Europaea identifiers sustainable, the use of the (much appreciated) data  import spreadsheet was stopped in 2009, because loading the spreadsheets required the removal of existing data set, including the taxonomic record IDs, which are the basis for the identifiers.
Gaps in data and knowledge After the first release of Fauna Europaea in 2004, the completeness of the received taxonomic data was calculated to include 99. 3% of the known European fauna (actual number of databased species 128,692; estimated number of described species 129,647). The faunistic coverage is less complete, but nevertheless including 90-95% of the total fauna. Recognised gaps for major groups are given in Table  2  (figures from 'unfinished work' provided by the experts after the first release). The analysis of existing gaps is quite essential for evaluating the quality of the contained information and to provide evidence for the need to constantly curate and update the database. Particularly as there is ongoing work being done to close the gaps, a thorough gap analysis helps to outline taxonomic groups where increased efforts are needed, not only for the involved coordinators and involved specialists but also for future European biodiversity projects that are focusing on biodiversity information.
The data-entry figures show a delay of around four years for entering newly described species into the Fauna Europaea database. Considering an average yearly increment of newly described animal species for Europe of around 670 species (figures from the Fauna Europaea gap analysis Suppl. material 4), the current taxonomic completeness is predicted to be 97. 5% (actual number of databased species 132,100; estimated number of described species around 135,400). According to figures of the Zoological Record, however, the average yearly increment of newly described animal species in Europe could be less, namely around 220 species, although the description rate of new species significantly differs within and between taxonomic groups (Fig. 9 ). The delay factor indicates that experts prefer to focus on adding older names (not yet included) or improving existing records (for instance refining distributional details), instead of including new species.
This could be caused by the often unclear taxonomic 'robustness' of newly described species (new names are quickly synonymised) plus the yet unavailability of sufficient occurrence details (newly described species have a poor distributional record). It could also reflect the rather slow uptake of new taxonomic information, which could be improved when experts are served with instant information (e. g. via RSS feeds) on publications of relevant new species. Gaps in taxonomic knowledge (species unknown to science) have been surveyed using indirect methods, with help of additional data from the European faunal inventories as recorded in the Biosis database  (Suppl. material 4, Fontaine et al. 2012). The analyses show an almost saturation for well known groups, like the vertebrates or Lepidoptera (only few new species remaining to discover), whereas for many other groups, like the Acari and Collembola, the analysis reveal gaps in knowledge or a decline in description rates, even for insects with an important economic impact, like the aphids.
In terms of gaps in geographic coverage Portugal and the Balkan states are the most prominent areas. Fauna Europaea contributed to various gap assessments in ensuing projects, like PESI (Suppl. material 12). In the PESI project also special emphasis was given to the quality and completeness of data on target species, meaning species featured on lists for species conservation, quarantine control or pest management, which resulted in a separate search routine in the PESI portal (Fig. 13 ). In the recently started EU BON project  (Hoffmann et al. 2014 ) further steps will be made by using Fauna Europaea as a basic tool for biodiversity assessment and for taxonomic expertise evaluation and management in Europe. In Projects like EU BON, Fauna Europaea is used as a standard reference for taxonomic Information.
Vernacular names Common names are the most important search terms for non-professional users to retrieve biodiversity information. By means of the extended network of national Focal Points (more then 50 Focal Points in around 40 countries), the PESI project was used to harvest additional information on European species (images, literature, conservation status, etc. ). Thus the PESI portal became a major meta-data repository for local biodiversity information and resources, including a resource for non-scientific names of species with 183,622 common names in 105 languages, also accessible via the Fauna Europaea portal. This information is also shared with Europeana as part of the OpenUp! project.
Governance and property rights To handle the (co-)ownership of experts over their data, all Fauna Europaea specialists became members of the Society for the Management of Electronic Biodiversity Data (SMEBD). In addition, a Fauna Europaea executive committee was installed (to deal with Fauna Europaea related agreements) and Fauna Europaea members (experts and management staff) were elected for SMEBD council functions at a regular base. The role and relevance of SMEBD as a professional society of authors of biodiversity databases, defending the interests of its members on issues like copyright and intellectual property rights, attempting to provide a legal basis for their protection and keeping a common agenda on the sustainability of taxonomic databases, is currently under evaluation (e. g.
Costello et al. 2014 ). In the next phase of Fauna Europaea stronger governance from internal Fauna Europaea committees is expected in place of cooperative attempts (like SMEBD). However, on defending property rights to external users, the relevance of the SMEBD organisation, experts or database custodians not protecting property rights as individuals, but lined-up as a consortium, is still of great (symbolic) value. Recently it was decided to further license the Fauna Europaea data under CC BY SA version 4. 0. We realise that most (if not all) parts of Fauna Europaea are not truly copyrightable (see  Egloff et al. 2014). Also licensing doesn't prevent any (intentional) misuse or violation of copyright or property rights, but having a good license policy in place at least facilitates a proper application by the willing users and could ease the maintenance of the rights, especially on proper attribution and acknowledgement.
2014)  to name a few. 'Contributions on Fauna Europaea' is the second series launched by Biodiversity Data Journal after the Checklist of British and Irish Hymenoptera  (Broad 2014 , Broad and Livermore 2014a , Broad and Livermore 2014b , Liston et al. 2014 ) and the first one that embraces thematic data papers structured in a common pattern extracted from a large data base. This novel publication model will assemble in a single issue 57 data papers on different taxonomic groups covered by Fauna Europaea project in the period 2000-2014. This is the first collection of data papers of this scale. It will formalise and effectively publish the scientific contributions of more than 400 experts building the largest European animal database. The new publication model provides a reliable mechanism for citation and bibliographic indexing of large and uniformly structured databases.
We hope that this pilot by Fauna Europaea will soon be followed by other similar large-scale databases, such as the European Register of Marine Species and Euro+Med PlantBase. In the future publishing data papers is foreseen as a standard practice after updating a Fauna Europaea data set. Apart from the value of producing scientific output and creditability for the Fauna Europaea experts, this will also allow for a more dynamic way of (additional) expert involvement, because all relevant contributions can be more easily acknowledged than what is currently the case, which will strengthen the ownership feeling (by producing 'legacy reports' for each version) and the sense of communality. In addition, the data papers satisfy the need for the provision of more background information on Fauna Europaea data sets, including for instance the used resources and expertise and on the taxonomic decisions taken.