dataesr/scientific-paragraphs-categorization huggingface dataset:
creatis. insa-lyon. fr)under research collaboration agreement. The source code is available at https://github. com/OpenCardiacMRISoftware/CMRSegTools. Funding: This work has been performed within the framework of LABEX  PRIMES 372  ( ANR-11-LABX-0063 ) of  Universitede Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) and  France Life Imaging  ( ANR-11-INBS-0006 ) operated by the  French National Research Agency (ANR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Sources and Software Usage The data used in this study mainly include land-use data, meteorological data, the digital elevation model (DEM), soil data, and the distribution of forest scenic spots. The land-use data used in this paper were obtained from the Data Center for Resources and Environmental Sciences, and Chinese Academy of Sciences, and they were divided into seven categories (Figure  2 ). The meteorological data were obtained from the China greenhouse data system, and the basic meteorological data of the XSB basin were obtained by using the ArcGIS 10. 2 software spatial interpolation tool for meteorological data. The forest scenic vector data were derived from Google Earth software. The basic spatial unit of this study is a 30 m resolution grid. The software and modules used in each single-factor evaluation are shown in Table  A1  of Appendix A.
Among them, water conservation and habitat quality were measured by the water yield and habitat quality modules of the integrated valuation of ecosystem services and trade-offs (InVEST) model  [29]. The carbon fixation and oxygen release were measured by the NPP estimation plug-in of ENVI 5. 3 software. The importance of the abovementioned ecosystem service functions was classified into 5 levels and assigned values from 1 to 5. The higher the value, the higher the ecosystem service level. For the forest scenic area correction, we downloaded the vector data of the XSB basin forest scenic area, assigned 5 to the forest scenic area, and assigned 1 to other areas. The evaluation results of the importance of ecological services were obtained by superposing the equal weights of the classified ecosystem services.
The importance classification was carried out by using the natural breakpoint method, and the results are generally important, relatively important, moderately important, highly important, and extremely important, marked as 1, 2, 3, 4, and 5, respectively  [30]. (a) Evaluation of water conservation function: The meteorological data of 87 meteorological stations in five provinces on the east coast of China in 2017 were collected by the China Greenhouse Data System, including precipitation, real-time atmospheric pressure, air humidity, wind speed, and temperature. The basic meteorological data of the XSB basin were obtained by using the Arcgis10. 2 software spatial interpolation tool. Evapotranspiration data of the XSB basin were retrieved by the surface energy balance principle (SEBS) model of ILWIS open 3.
8. 1. 0 software. The data on soil depth, soil texture, and soil organic matter content were obtained from the national soil database, and the available water content of plants (PAWC) was calculated. By referring to the reference value of the crop coefficient of FAO and the research results of other scholars  [31] , we obtained the biophysical number table of each land-use type (Table  1 ). Finally, the Water Yield module of the InVEST model was used to import all data to calculate the water conservation data of the XSB basin  [32].
(b) Evaluation of carbon fixation and oxygen release (NPP): Based on the meteorological interpolation data obtained in the process of water conservation calculation, the radiation data and vegetation type data in 2017 were collected, and the NPP grid data of the XSB basin were calculated through the NPP inputs of ENVI 5. 3 software (CASA model). The static parameters of the data required for the model are automatically configured by the software. The vegetation-type map, NDVI data, and meteorological data were collected from the Internet. (c) Habitat quality assessment: The habitat quality was obtained by using the habitat quality module of the InVEST model, in which cultivated land, rural residential areas, urban residential areas, roads, and industrial land were regarded as threat sources.
The value is between 0 and 1. The closer the value is to 0, the smaller the sensitivity, and vice versa. Habitat suitability is a habitat score given to each land type, with a range of 0-1. The higher the score, the better the habitat suitability of the land type. The habitat suitability of various habitats and the related parameters of their sensitivity to threat factors were mainly based on the research results of other scholars  [36]. The habitat quality map was generated by the distance attenuation method and exponential attenuation method. Vector data of forest scenic spots can be downloaded through Google Earth. The data of forest scenic area were used as correction factors after the superposition of water conservation, NPP, and habitat quality. (2) Ecological sensitivity: Ecological sensitivity assessment is a comprehensive assessment of the possibility and degree of ecological imbalance and environmental problems when human activities interfere in a certain area.
(3) Landscape connectivity: Landscape connectivity is a measure of the continuity between landscape spatial structural units. High landscape connectivity is conducive to the maintenance of ecological processes and biodiversity. This study analyzed the landscape connectivity through the morphological spatial pattern analysis method (MSPA); classified the landscape by using guidos Toolbox 2. 8; took ecological land such as forest land, wetland, grassland, and water as the foreground; and took other non-ecological land as the background. After obtaining the grid map, ArcGIS 10. 2 software, the plug-in modules Conefor inputs, and Conefor Sensinode 2. 6 were used to calculate the landscape connectivity. The overall index of connectivity (IIC) and the probability of connectivity (PC) were used to evaluate the connectivity of the landscape in the study area  [39].
Digital botanical collections are increasingly being used in phenological research  [27] [28] [29] [30] [31] [32] , studies on species extinction  [33] [34] [35]  and invasion  [36] , and modelling species distribution  [37, 38]. Interest in the information archived in NHCs has increased significantly due to threats to biodiversity  [39]  and global climate change  [40] [41] [42]. Digital biocollections have proven to be useful in nature conservation  [43] , and open access to biodiversity databases is conducive to the development of citizen science and natural education  [44] [45] [46]. On a global scale, this demand is being met by large international initiatives, such as the Global Biodiversity Information Facility (GBIF)  [47] [48] [49] [50]  and Integrated Digitized Biocollections  [51].
They bring together hundreds of regional and local collections in their networks, in which digital databases of biodiversity are based on common international standards. Many regions are underrepresented in global biodiversity databases, and their collections are still seeking a place in the digital information flow  [52]. With this in mind, we would like to share our experience in developing a digital biodiversity collection aimed at a wide range of users. The functional Scientific, Educational, Public, and Practical Use (SEPP) Model proposed in this paper assumes that digital data on biodiversity will be used for scientific, educational, public, and practical purposes. In this study, we aim to present an original information system on biodiversity that (i) is based on scientific natural collections, (ii) covers a wide range of organisms (algae, plants, fungi, and animals), (iii) takes into account the needs of various user groups, and (iv) is linked to the international GBIF database.
Materials and Methods In this paper, we share our experience with the development and implementation of the project "AMU Nature Collections-online (AMUNATCOLL): Digitization and sharing of the natural data resource of the Faculty of Biology at Adam Mickiewicz University in Pozna ń"  [53]. The AMUNATCOLL project was cofinanced by the European Union through the European Regional Development Fund under the Operational Program Digital Poland (OP PC), Priority axis: II E-administration and open government; Action: 2. 3 Digital accessibility and usability of public sector information; Submeasure: 2. 3. 1 Digital access to public sector information from administrative sources and science resources. The idea of digitizing and sharing data on biodiversity was born at the Faculty of Biology of AMU (FBAMU), which for over 100 years has been a regional centre for biodiversity research and a storage site for NHCs.
The expectations signalled at that time were taken into account in the conceptual model of the AMUNATCOLL IT system. The system consists of a digital database and tool set made available through the web portal and a mobile application integrated with the portal. The AMUNATCOLL database is fed by four analogue information sources subject to the digitization process: (i) natural collections, i. e. , specimens of algae, plants, fungi, and animals, as well as a collection of soil samples from all over the world, deposited at the FBAMU; (ii) published and unpublished floristic, faunal, and mycobiotic data, mainly from Poland; (iii) nature photography and films documenting scientific research in many regions of the world; and (iv) bibliographic sources cited in the AMUNATCOLL database. The AMUNATCOLL mobile application allows users to document observations directly in the natural environment and send them to the portal for further analysis.
The portal is interactive, enabling users to explore the shared data and provide feedback to the system administrator. The system consists of a digital database and tool set made available through the portal and a mobile application integrated with the portal. The AMUNATCOLL dat is fed by four analogue information sources subject to the digitization process: (i) na collections, i. e. , specimens of algae, plants, fungi, and animals, as well as a collecti soil samples from all over the world, deposited at the FBAMU; (ii) published an published floristic, faunal, and mycobiotic data, mainly from Poland; (iii) nature ph raphy and films documenting scientific research in many regions of the world; an bibliographic sources cited in the AMUNATCOLL database. The AMUNATCOLL mobile application allows users to document observation rectly in the natural environment and send them to the portal for further analysis portal is interactive, enabling users to explore the shared data and provide feedback system administrator.
Interactivity with biodiversity data is an important feature of the system becaus AMUNATCOLL mobile application allows users to document observations directly natural environment and send them, including the digitized data, to their portal ac for further analysis. Users can also add their own archive location data directly to the in the portal. Additionally, using the features of creating teams and joining teams, can share field data and comments. The specimen data along with their occurrence, fully available at the A NATCOLL portal, are successively transferred to the international GBIF database [6 It is worth emphasizing that the AMUNATCOLL IT system is open to the inflow of from other research centres and individual owners of nature collections. The presented idea goes towards Digital Extended Specimen (DES)  [62]  conce respect of developed functionality as well as following FAIR principles (findability, a sibility, interoperability, and reuse) of specimen metadata.
The specimen data along with their occurrence, fully available at the AMUNATCOLL portal, are successively transferred to the international GBIF database  [60, 61]. It is worth emphasizing that the AMUNATCOLL IT system is open to the inflow of data from other research centres and individual owners of nature collections. The presented idea goes towards Digital Extended Specimen (DES)  [62]  concept in respect of developed functionality as well as following FAIR principles (findability, accessibility, interoperability, and reuse) of specimen metadata. The DES concept was developed through an open community consultation led by GBIF  [63]  and is a result of aggregation of the open digital specimen concept developed under DiSSCo initiative  [64]  and the extended specimen concept developed by Lendemer  [65]  and implemented by Biodiversity Collections Network  [66].
Collecting and Sharing Biodiversity Data in the AMUNATCOLL IT System The main path from nature collections and information being deposited in the traditional (analogue) way at the AMU to making them available in a modern and fully open format leads through the following stages: digitization, database design, and portal equipped with search and analysis tools (Figure  3 ). The AMUNATCOLL database, in a special private area, can also be supplied with digital data sent from a mobile application that has been specially developed for field surveys. format leads through the following stages: digitization, database design, and p equipped with search and analysis tools (Figure  3 ). The AMUNATCOLL database special private area, can also be supplied with digital data sent from a mobile applic that has been specially developed for field surveys.
Metadata Structure and Database The core of the AMUNATCOLL IT system is a database with content that is primarily on collections of biological specimens as well as photos and published or p ously unpublished field observations (Section 1 in Table  1 ). These analogue biodiv documents are described during digitization with metadata that meet ABCD 2. 06 s ards  [67]  and correspond to the Simple Darwin Core Categories  [68] : Record-level T Taxon, Occurrence, Event, and Location. Depending on the type of document, the nu of fields in a single record varies from 68 to 85 (Table  1 ). For the purpose of the specimen description, fields for information on the loc and method of storage and conservation are also provided. A specific source of mation on biodiversity is samples containing taxonomically unidentified biological rial (mainly invertebrates).
These very valuable samples await scientific analysis, pa larly the determination of their species (Section 2 in Table  1 ). An extensive descript the samples includes, among other things, information about already elaborated sy atic groups that allows researchers to focus their analyses on groups that have n been identified. The list contains 50 metadata items and is modelled on Section 1 AMUNATCOLL database also collects multimedia documents of landscapes and hab as well as photos of specimens, which, although they do not have all the attributes n sary for inclusion in Section 1, can be used for nonscientific purposes (Section 3). I case, a single record consists of 51 fields. Section 4 (References) contains 28 metada scribing the references and other source documents cited in the descriptions of speci photos, biological samples, and multimedia documents.
Metadata Structure and Database The core of the AMUNATCOLL IT system is a database with content that is based primarily on collections of biological specimens as well as photos and published or previously unpublished field observations (Section 1 in Table  1 ). These analogue biodiversity documents are described during digitization with metadata that meet ABCD 2. 06 standards  [67]  and correspond to the Simple Darwin Core Categories  [68] : Record-level Terms, Taxon, Occurrence, Event, and Location. Depending on the type of document, the number of fields in a single record varies from 68 to 85 (Table  1 ). For the purpose of the specimen description, fields for information on the location and method of storage and conservation are also provided. A specific source of information on biodiversity is samples containing taxonomically unidentified biological material (mainly invertebrates).
These very valuable samples await scientific analysis, particularly the determination of their species (Section 2 in Table  1 ). An extensive description of the samples includes, among other things, information about already elaborated systematic groups that allows researchers to focus their analyses on groups that have not yet been identified. The list contains 50 metadata items and is modelled on Section 1. The AMUNATCOLL database also collects multimedia documents of landscapes and habitats, as well as photos of specimens, which, although they do not have all the attributes necessary for inclusion in Section 1, can be used for nonscientific purposes (Section 3). In this case, a single record consists of 51 fields. Section 4 (References) contains 28 metadata describing the references and other source documents cited in the descriptions of specimens, photos, biological samples, and multimedia documents.
Two sections (1 and 3) also include six fields for information on the usefulness of digitized objects for specific user groups. The database implemented in AMUNATCOLL IT provides effective access to all metadata fields as well as organizational, technical, and auxiliary fields. An open-source PostgreSQL server was deployed in the system. It manages relational databases and emphasizes extensibility and SQL compliance. PostgreSQL is designed to handle heavy workloads, such as data warehouses or web services with multiple concurrent users. Furthermore, it is often the primary choice for solutions for biodiversity systems, as is evidenced by examples of institutions such as the Belgian Biodiversity Platform  [69]  and Island Conservation Society  [70]  or the TriatoKey application  [71]. The database is divided into three logical parts: (i) "amunatcoll", responsible for storing digitized textual data on specimens; (ii) "dlibra", which stores metadata on digitized images of specimens and multimedia collected directly in the field; and (iii) "anc_portal", responsible for the storage of data related to the portal and its functions.
Portal The architecture of the AMUNATCOLL portal  [72]  is in line with the assumptions of the conceptual model of the IT system (Figure  2 ). It takes into account both the abundance and complexity of information on biodiversity resources deposited in the database as well as the requirements of potential users. The modular and multilayered structure of the portal offers users quick and easy access to data while enabling the administration panel to manage the system. The homepage contains basic information about the AMUNATCOLL IT system, including database resources, Geographic Information System for Biodiversity (BioGIS) tools, the mobile application, potential users, and the contractors and owners of the system (Figure  4 ). as the requirements of potential users. The modular and multilayered structure of the portal offers users quick and easy access to data while enabling the administration panel to manage the system.
The homepage contains basic information about the AMUNATCOLL IT system, including database resources, Geographic Information System for Biodiversity (BioGIS) tools, the mobile application, potential users, and the contractors and owners of the system (Figure  4 ). A much more extensive description, including the mission of the system, can be found in the "AMUNATCOLL" module. However, the most important functions for the user are in the Search Engines, Tools, and My AMUNATCOLL (Figure  5 ) modules. The system allows users to search for information about specimens, biological samples, multimedia documents, bibliographic sources cited in the database, and educational materials. Specimens and taxa can be viewed using several types of search engine. The general search engine is available to everyone, while the others are available to logged-in users.
In the simple and extended search engines, the "and" condition is used by default. On the other hand, in the advanced search engine, the operators "or" and "no" are also implemented and when used are explicitly mentioned and modified. Specimens can also be accessed by selecting collections or systematic groups, the lists of which are made available in the panels of relevant search engines. Systematic groups are represented by higher rank taxa, for which information is not included in the metadata specification itself. Data on taxonomically unidentified samples can be obtained using 49 selection criteria classified into four groups (Figure  7a ). Eight criteria were entered for the multimedia search, and six criteria were entered for the bibliography (Figure  7b, c ). Authorized legitimate users can create educational and training materials from AMUNATCOLL database documents that are available to everyone and can be downloaded from the successively completed list.
Data on taxonomically unidentified samples can be obtained using 4  The "Tools" module, which is closely related to the search engines, incorporates tw functions: creating statistical reports and visualizing the results of analyses on charts an maps. A detailed description of these tools is beyond the scope of this paper, so we use a example of an analysis aimed at answering the following questions: (i) How many an which scans of Papaver rhoeas specimens are represented in the AMUNATCOLL botanica mycological database collection? (ii) In which countries were they collected? (iii) How ar these sites distributed? After three search criteria (genus/species, iconography, and collection type) are se lected, the number of specimens that meet them is displayed (Figure  8a ). Adding th grouping factor (country) allows the user to obtain a table with the number and percen age distribution of the specimens and to generate appropriate charts (Figure  8b ).
Usin BioGIS tools, the obtained results can be presented on various types of maps (e. g. , do distribution map, cluster map) (Figure  8c, d ). This opens up wide possibilities of analysi built upon the BioGIS tools selected by the user according to his or her needs. The "Tools" module, which is closely related to the search engines, incorporates two functions: creating statistical reports and visualizing the results of analyses on charts and maps. A detailed description of these tools is beyond the scope of this paper, so we use an example of an analysis aimed at answering the following questions: (i) How many and which scans of Papaver rhoeas specimens are represented in the AMUNATCOLL botanicalmycological database collection? (ii) In which countries were they collected? (iii) How are these sites distributed? After three search criteria (genus/species, iconography, and collection type) are selected, the number of specimens that meet them is displayed (Figure  8a ).
An ecologist documenting natural habitats has the option of selecting habitat types from the list provided or entering names according to individual classification. A special form is available for descriptions of zoological samples, such as soil samples, litter, bird nests, and deadwood. The geographic locations of the field observations saved in the application and the collected samples can be displayed at all times on the map so that the user has a constant view of his or her location while working in the field. To deliver even more control over fieldwork, the application can remember the user's course over the route. The mobile application can be linked to an individual user account established on the AMUNATCOLL portal. Data transferred to a personal account can be processed using all analytical tools implemented on the portal.