dataesr/scientific-paragraphs-categorization huggingface dataset:
BasExp allows for such connections. Among other changes in biodiversity databanks, another one is worth mentioning: they are currently evolving toward less taxa-centered architectures. Many databanks, especially those related with natural history museums, are organised along taxonomic group divisions: one databank for flowering plants, one for crustaceans, etc. However, as emphasized above, there is an increasing need to access a synthetic representation of biodiversity that overcomes the intrinsic limits of taxa-oriented databanks. As biodiversity has to be captured according to many different aspects  (geographical, dynamical, taxonomic, genetic, etc. )  at once, some databanks offer scientists the means to question their data according to several criteria. BasExp nicely illustrates this possibility.
In particular, as there has been a huge effort within BasExp to homogenize geographical data about sampling locations, which were difficult to find out and exploit in the past, it provides researchers with a new and long-awaited type of ready-to-use information. Outside BaxExp, geographical information varies in format and degree of precision from one collection to the next within the Paris Muséum. Gathering all information on a given location and standardizing its format is thus an important advance in itself. Moreover, before the establishment of BasExp, each collection databank had its own data system: they did not use the exact same names for expeditions and did not have the same degree of precision for geographic location. By now, BasExp is the geographical data provider for all the Paris Muséum's collection databanks.
Databanks vs. Catalogs In this section, we put forward a systematic comparison between library or collection catalogs and databanks in order to make clear what the specific features of databanks are from the users' point of view. We shall show that far from being improved catalogs, allowing for gain in time, databanks are flexible tools that play new roles in the development of biodiversity knowledge. An important difference between databanks and catalogs is that databanks allow for more than one guiding principle with respect to organisation of information. Let us first explain what we mean by a "guiding principle" with respect to organization of information. In a museum of natural history, catalogs are usually designed according to the way specimens' identifications are produced. By contrast, an internet-connected databank may be organised according to several guiding principles: specimens' names, geographical localization, gene sequence, date of discovery, name of discoverer, etc.
The organisation of information along multiple dimensions, all of them of scientific interest, is an efficient way to disclose connections that remain inaccessible to catalog users. A nice illustration of this important feature of databanks is that whereas the absence of a species at some place cannot be inferred from consulting a catalog, it may be discovered by cleverly questioning relevant databanks. The main reason for that difference is that catalogs based on specimen identifications cannot but register the presence of specimens without leaving any opportunity to discover information about absence, whereas a databank organised by geographical location may disclose information about absence. Let us mention another difference between databanks and catalogs, which revolves about their users.
Usually, the users of a catalog are determined before its implementation. For instance, the users of the catalog of a museum's collection are often meant to be people working at the museum, most of them taxonomists: catalogs are most devised for local use. By contrast, internet-based databanks are usually used by different users, even more so when they are inter-connected by means of an Information System, namely, a network of devices for the acquisition, organization, storage, and communication of information that is developed and managed by the host institution. The users' variety forces databanks' designers to conceive the organisation of information in such a way as to push the boundaries of local use. The needs and interests of local users of a catalog, e. g. , the members of a museum's scientific community, are more easily identified and narrower than the needs and interests of external users.
Taking the latter into account forces databanks' designers to introduce new possibilities of investigation, e. g. , new query types or combinations. This is a very difficult task indeed, as emphasized by Leonelli in the case of biomedical databanks: "[i]ncorporating a large variety of possible viewpoints and prospective queries has been, and continues to be, the most complex and labourintensive task involved in the development of [the databanks]"  (Leonelli 2013a). It amounts to try to guess what the new directions of research may be in order to make the databank usable and useful in the future. This anticipatory task can be said central in the conception and design of biodiversity databanks, whose role in the development of biodiversity knowledge will increase. In Sect. 3. 3. 3, we further explore the implications of the way information is organised within a databank by showing how their underlying organisational principles may foster the evolution of scientific knowledge.
Databanks' Organization and the Dynamics of Biodiversity Knowledge The principles and functioning of databanks are too often ignored by philosophers of science, who tend to view them as black boxes, whereas looking inside allows one to discover valuable information about the way scientific knowledge is produced on a daily basis. This is why we aim at opening these black boxes and describe their internal functioning in order to show how information is acquired and transformed within them. We will illustrate our claims about the way data are typified and organised by a series of examples. Each databank's organization obeys a dominant organisational law that is based on a specific type of information. For instance, as mentioned above, some databanks are centered on taxa whereas others are centered on geographical areas: these are examples of types of information.
Other types of information include pictures, taxonomic papers presenting taxonomic descriptions, papers presenting taxonomic revisions, or genetic sequences. It is worth emphasizing that the notion of "type of information" we introduce here is defined with respect to the organisation of biodiversity databanks. For sure, pictures of specimens and taxonomic papers may contain information on the same organisms; however, the information contained in a picture will not play the same role in the process of knowledge-production as the information contained in a taxonomic paper. The databank user will not use a picture in the same way she uses a species description. This is why our notion of type of information is not defined with respect to the item in the world that the information is about, but with respect to the way the information is used by the databank's user.
Our notion of type of information allows us to establish a classification of biodiversity databanks into taxonomy-oriented, geography-oriented, picture-oriented, revision-oriented, etc. databanks. The type of information that associates a given databank to a class governs what we call the "organisational law" that defines a specific kind of links, within the databank, between the type of information that is the most important within it and other types for information. For instance, the organisational law of a taxonomy-centered databank relates taxonomic data with geographical data, data about endemism, genetic data, etc. The organisational law thus sets apart a center and a periphery within the databank. Center and periphery are defined relative to types of information, as defined above. Let us make clear how this center-periphery organisation is implemented in various examples.
(i) We begin with WoRMS, the World Register of Marine Species (http://www. marinespecies. org/, already mentioned above). The organisational law for this databank connects original and current species names. As taxonomic revision is at the heart of taxonomic practice, it is highly important to keep track of revisions in order to avoid collective oversight, which would have devastating consequences. WoRMS contains, for each species name, the list of its revisions, dates, and names of associated taxonomists. The use of WoRMS is name-oriented; it delivers information on the sequence of past revisions. It is an international databank. Peripheral information is, e. g. , the geographical distribution of the species. (ii) The OBIS geographical databank (Ocean Biogeographic Information System, http://www.
iobis. org/) is sampling-event and geography-oriented, and specific to marine species; it also provides physical, chemical and topographic information on precise locations where specimens have been observed or sampled. (iii) GBIF (Global Biodiversity Information Facility, http://www. gbif. org/) is a sampling-event databank mapping signalization of species (observation-or sample-event) all around the world. (iv) By contrast with GBIF, let us mention INPN (Inventaire National du Patrimoine Naturel, https://inpn. mnhn. fr/accueil/index), which also maps signalization of species (observation-or sample-event) on the French territory, with an emphasis on landscape preservation. (v) BoLD, already mentioned above, combines genetic sequences with information on individual specimens. It is important to emphasize the differences between BoLD and GenBank, another gene-centered databank (see (vi)).
Contrary to BoLD, GenBank is centered on the link between genetic sequences and scientific publications. There is no link with actual specimens within GenBank. By contrast, the users of BoLD look for a genetic sequence and the associated species name and may check themselves, by performing standardized experiments, whether the stored genetic sequence (1) has been correctly established and (  2 ) is associated with the right species name. The users of GenBank are not given this possibility because GenBank provides them with a link between a genetic sequence and a scientific paper; as a result, they cannot but trust the authors of the paper with respect to the validity of the gene sequence and associated species name. By contrast, the genetic sequences in BoLD may be not published  (Ratnasingham and Hebert 2007).
The difference between BoLD and GenBank illustrates an important division among biodiversity databanks: those that provide links to actual specimens, allowing data-checking by the users, and those that provide links to publications or sampling-events, which oblige the users to trust the original information providers. (vi) The users of GenBank (https://www. ncbi. nlm. nih. gov/genbank/) are looking for comparisons between their own organisms of interest and model organisms whose genomes have been sequenced and included in the databank. It includes all the sequences that have been published in scientific papers. (vii) Let us end this list with two examples from the Paris Muséum National d'Histoire Naturelle. COLLECTIONS is the databank of the Muséum's collections, whose functioning is explained here: http://collections.
mnhn. fr/ wiki/Wiki. jsp?page=Publication_Internet_en. Each museum has several collection databanks for fish, mollusks, crustaceans, etc. They are specimenoriented: the users look for a specimen's number (called "voucher ID") or a name, in order to get information thereon (nomenclatural status: holotype, paratype, etc. ; sample site; name). (viii) BasExp: As mentioned above, this databank is expedition-oriented: the users look for information related with a given expedition. This databank allows for both non-taxa-oriented research and a global appraisal of an expedition in terms of sampling location, specimens, papers, and reports. It is the repository for geographical data related with expeditions. Whereas each databank has its own organisational logic, many biodiversity databanks function collectively by means of a series of networks like GBIF.
Networks of databanks obey their own laws that govern data flow. Usually, data flow runs from the most standardized and best validated data to lesser controlled databanks. Data flow is thus a powerful way of enhancing and homogenizing standards because downstream databanks, when they happen to become upstream relative to other, more local databanks, have to strengthen the reliability and accessibility of their data. Establishing a connection between two biodiversity databanks is indeed a common way to fill the gaps within each. It also contributes to improve data validation and warrant traceability: as emphasized by  Costello and Vanden Berghe (2006) , "[g]lobal databases that integrate information on species force the development of standard classifications". Databank networking has a further important effect at a higher level of knowledge production: it makes clear that some areas of knowledge that had been considered independent beforehand actually entertain epistemic relationships that are now considered as pivotal.
For instance, the links between gene sequencing and species identification, implemented in BoLD, has only been brought to light recently (compared with the long history of species identification and the less long, but still not so recent, history of gene sequencing). Realizing that gene sequencing may facilitate species identification has been a side-effect of developing genetic databanks and databanks networks. BoLD's strength, in this context, is to allow data-checking by providing links with actual specimens (which do not change over time), whereas other databanks depend on taxonomic descriptions, which are hypothetical and susceptible to be transformed. Another important tendency in the evolution of the way biodiversity databanks get organised is the gradual expansion of the domain of relevant data.
Many elements that were considered irrelevant for biodiversity knowledge are now emerging as constitutive, and thus worth collecting, taking care of, and connecting with other types of data. For instance, BasExp includes information on the extraction device of marine specimens, which it relates with sampling sites as well as with digital pictures illustrating the collected specimens in their substrate. The pictures shot during the expeditions have long been difficult to access by scientists who were not on board; now, their status as vehicles of knowledge has completely changed in the last few years as they became easily accessible within databanks and provide irreplaceable information about either the specimen's environment or the context of its extraction. BasExp thus nicely illustrates a trend that has been identified by  Leonelli (2013a) , who emphasizes that the databanks she examines (The Arabidopsis Information Resource, TAIR and the cancer Biomedical Informatics Grid, caBIG) progressively include elements that were not considered important, like archives of data provenance (the methods and instruments originally used to generate data) and links to biological materials.
We cannot but agree with Leonelli's statement that "setting up and updating these resources occupies much of curators' time and creative efforts". A last point has to be made about the connection between the development of biodiversity databanks and the dynamics of biodiversity knowledge. It is about the pace of transformation. The different features of biodiversity databanks do not usually evolve at the same rate, which dictates a mandatory upgrading process. For instance, paper catalogs of large natural history museums have usually not been computerized at once, but rather step by step. Now, each step in this process of computerization took place within its own technological and scientific context, involving innovations that the next steps had to catch up with. Evolving biodiversity databanks is thus no linear process.
Some events had large interfering consequences, like the establishment of the BoLD consortium (as described here: http://www. barcodeoflife. org/content/about/what-cbol). As soon as Barcoding of Life proved a useful and fruitful endeavour, each biodiversity databank had to take this program into account, which meant huge adaptive changes. Adapting databanks moreover involves satisfying basic requirements of cumulativeness, editability, and interoperability, as these requirements ensure homogeneous development. In this section, we have presented and discussed the logic governing the organization of biodiversity databanks by means of examples. We have also emphasized the importance of their interconnection into networks and the interplay between their evolution and the changes within biodiversity knowledge.
On the Properties of Useful Biodiversity Databanks: Concluding Remarks Let us recall that, in this paper, we call a biodiversity databank "useful" when it provides scientists, managers of assessment programmes, and conservationists with the means to successfully achieve their aims. In this section, we present two types of requirements a biodiversity databank has to satisfy in order to be useful in this sense. The first type is more on the technical side (although it is not content-independent) whereas the second is linked to the distinguishing features of biodiversity knowledge. As mentioned above, a few basic requirements have to be met in any databank that is meant to be used by scientists. First, data have to be standardized. Standardization involves defining different data types (i. e.
, building up a glossary) in order to optimize interoperability among databanks. We can now bring to light the peculiar difficulties raised by this operation by coming back to the example of the integration of non-taxonomic data in a biodiversity databank, namely, data coming from biological and ecological descriptions (cf. Sect. 3. 2. 2). In order to include biological and ecological traits within a databank for marine species, the main challenge is to identify which traits are (1) useful and (2) available to researchers and conservationists. This is the first step of data standardization, which obliges databank designers to struggle with linguistic subtleties: For example, "littoral" habitat can be the marine zone between the low and high tide marks, extend to the continental shelf and include coastal river catchments, and refer to the edge of freshwater lakes.
The lack of standard use of terms can compromise the bringing together of this knowledge from different sources. (Costello et al. 2015). We see in this example that standardization forces databank designers to formulate precise definitions of the terms they choose to use within the databank, as well as to determine measurement units. This is a crucial, non-trivial step in the elaboration of the databank. It is based on the previous identification of the set of terms that are used in other databanks and in the relevant literature. Standardization is not always possible, however: when the way data are produced remains unknown, i. e. , when the databank designer does not know whether they are primarily found in scientific papers, reports, other databanks, or unpublished sources, they cannot possibly be standardized because standardization involves checking the validity of data.
However, when data are from unknown origin, their validity cannot be properly checked. That is the reason why working groups for data standardization must involve scientists: standardization is knowledge production. The most important requirement to build up a useful databank is that data have to be validated: they must be submitted to a process that provides scientists with the same kind of warrant as the peer review process so that they can trust the elements they retrieve from databanks. When checking the validity of data, users are assisted by the meta-data that provide them with contextual information allowing them to both evaluate reliability and quality of data. Meta-data are sets of data that accompany the constitutive data of a databank: for instance, in a taxonomy-oriented databank, the constitutive data may be species descriptions and associated meta-data geographical information, gene sequences, pictures, location of holo-and paratypes, etc.
When links to actual specimens are provided, databank users can moreover check themselves the validity of the data they are interested in. These links are thus an important way to enhance the collective process of data validation, and, as a result, the overall scientific quality of biodiversity data and databanks. The usefulness of a databank is conditioned by the basic requirements so far illustrated and by the definition of possible queries. By listing the words or expressions that constitute well-defined queries, databank designers identify and delineate the possible uses of the tool. As mentioned above, the databank designers' role is to guess the future uses of the databank and to anticipate the developmental paths of biodiversity knowledge. This can only be done by people in close contact with on-going research, assessment, and conservation practices: even though it may not always result in the publication of scientific papers, databank design and main-tenance is genuine scientific work because it implies being well aware of the scientific state of the art at a given time, of how the relevant scientific community assesses the various scientific hypotheses at stake, and of the emerging links among different fields of research.
Even though biodiversity databanks already have a history, they are recent tools compared to the old practice of publication of papers and monographs. A major challenge for biodiversity databanks is to become a part in a set of older practices that themselves transform at a quick pace. This means that biodiversity databanks should connect their contents with other, older epistemic practices. More precisely, they should complement, rather than replace, more traditional reservoirs of biodiversity knowledge, like collections of natural history, because reliable biodiversity knowledge requires links with actual specimens. This is an important and difficult task for databanks designers and associated computer scientists and engineers.
BOLD Barcode of Life WoRMS World Register of Marine Species EOL Encyclopedia of Life IBOL International Barcode of Life Project WoRDSS World Register of Deep-Sea Species Darwin Core Ratification of Darwin Core 1999 2000 2001 2004 2007 2009 2010 2012 COML : Census of Marine Life Marine Barcode of Life OBIS COML GBIF Ocean Census of Global Biogeographic Marine Life Biodiversity Information System Occurrence Data and Physical and chemical data. Fondations Sloan Taxonomic data, geographical data & ecological data Sloan Fondations Information Facility Taxonomic data & geographical data collection data) Data Provider for IPBES data and (observations Barcode library for all eukaryotic life ID number (Voucher specimen), taxonomic assignment (could be phylum level) & sequences List of valid names with Museum -COML -Collection Databases from Data Provider: the original name and original description Financed by European Union Barcode of Life Data Systems (BOLD) reference library Extending the geographic and taxonomic coverage of the barcode data available from Worms to taxonomic characteristics ecological Thematic species database: Add image and other recommandations Data Provider: Data Provider: OBIS and WORMS First version Taxonomic data and biological as a Standard data with illustrations, videos Sloan Fondations Fig.
1992 Convention on Biological Biodiversity BioNet International 1993 1994 1998 TDWG Global Initiative Taxonomy Taxonomic Database Working Group Working group on Biological Informatics Technical cooperation To encompass taxonomic (OCDE) to Remove taxonomic impediment network to help build the databases, adressing data • coordinate taxonomists taxonomic capacity needed by < developing countries> standard • develop tools to link all the available biodiversity data of the differents databases.