dataesr/scientific-paragraphs-categorization huggingface dataset:
The diversity of living beings, of their behaviours and interactions is so huge that what is known about it may be compared to ancient maps in which the size of the known world is much smaller than the breath of the unknown world. Thus, in most cases, currently available knowledge of biodiversity cannot provide but a small contribution to the assessment of biodiversity. However, some parts of this knowledge are more readily useful for assessment tasks than others: the knowledge of biodiversity that is contained in databanks can be easily harnessed. But to what extent can the data in databanks be treated as knowledge? We discuss this question in the following, and emphasize again that retrieving this knowledge cannot be anything else than a small part of the assessment task. Learning about biodiversity is a complex endeavor.
First, because biodiversity itself is a complex object of knowledge. Second, because it extends on virtually every region of our planet, however small. Third, biodiversity knowledge comes from heterogeneous sources: taxonomic, evolutionary (including phylogenetic), genetic, and ecological research. This results in a confused picture in need of clarification. However, in the last few decades, biodiversity knowledge has immensely benefited from the establishment of international databanks. They play a fundamental role in the improvement of the current, confused picture of biodiversity because they provide scientists with elements from which they can develop knowledge of biodiversity at a the global scale. Our aim in this paper is to examine how biodiversity databanks contribute to developing current knowledge of biodiversity.
We do so by putting forward an epistemological analysis of the structure and functioning of databanks, both at the individual and collective (network) level. The epistemological analysis of scientific databanks has greatly benefited from  Leonelli's work (2010 Leonelli's work ( , 2013a Leonelli's work ( , b, 2016)). Whereas she focused on various fields within biology, she never addressed the topic of biodiversity databanks. With this chapter, we wish to fill this gap and participate in her effort to put databanks in their right place in contemporary biological science and assessment and conservation policies. The chapter is focused on marine biodiversity databanks and their role in the development of knowledge and assessment of biodiversity. Marine biodiversity is even less known than terrestrial biodiversity, thus illustrating the "ancient map flavour" of this domain of scientific knowledge.
By describing how marine biodiversity databanks are developed, we show what kind of knowledge they promote and how it may be used in assessment and conservation tasks. We begin by analyzing what it means to know about biodiversity. This question is raised because biodiversity is an unusual object of knowledge, crossing spatial and temporal scales. It is thus important to explore how a reasonably unified picture of biodiversity can be achieved by combining various components. Databanks con-tribute to this picture in an important way. We try to uncover which complex processes result in the "data" that are included in databanks and available for assessment and conservation tasks. We show that these data, far from being "brute data", are pieces of scientific knowledge subject to constant revision.
(2009)  detected two homogeneous genetic groups within the set of organisms called Lumbricus terrestris. This led  James and co-authors (2010)  to begin a new, systematic study that included 230 fresh specimens from Europe and North America (belonging to L. terrestris and other species in the Lombricus genus) and specimens that had been preserved by Savigny. This new study was both genetic and morphological; it took part in the Barcoding earthworms programme and its results have been integrated in BoLD and GenBank, which are two major international, genetic databanks (see below for more details about these databanks). James et al. showed that Savigny was right: there are two diverging groups within L. terrestris. A new revision, similar to the one put forward by Savigny, thus occurred. We are now left with two species of earth worms: Lumbricus terrestris and Enterion herculeus.
The main reason for this lack of unity is that each involved discipline has its own units, which are difficult to compare with one another. For instance, in population genetics, a gene may be considered a unit of biodiversity (but as is well-known, a gene in population genetics is not exactly the same entity as a gene in molecular biology and genetic databanks, which renders things even more complicated  (Baetu 2012; Carlson 1991; Falk 1986; Fogle 2000; Gerstein et al. 2007; Kitcher 1982; Sterelny and Kitcher 1988; Waters 1994). In conservation biology, it is not uncommon to count organisms as units of biodiversity. Populations, species, communities, ecosystems, or even landscapes are also relevant units of biodiversity in ecology and conservation biology. But how do these different units compare? There is no general theory yet that would be able to precisely connect genes with organisms, or a forti-ori with communities or ecosystems.
Improving Our Knowledge of Biodiversity via Cyber-Infrastructures In order to improve our knowledge of biodiversity, it is first necessary to establish what its vehicles are. By "vehicles", we mean the devices that are used by researchers to acquire and develop biodiversity knowledge. Among these vehicles, scientific papers play a major role, but they are far from being the only way to build up and complement biodiversity knowledge. Expert reports and outcomes of inventories and assessment tasks are also among the means that researchers can rely on, as well as gene sequences and specimens in natural history collections. Inventories, gene sequencing, collective scientific expertise, results of assessment endeavours, and collection management are key vehicles of biodiversity knowledge. As most of their outcomes are made available as "data" within databanks, databanks are indispensable vehicles of biodiversity knowledge as well.
The "data" they contain are immensely diverse, from gene sequences to species descriptions, taxonomic revisions, geographical localisations, etc. "Data" is thus an ambiguous term that has to be further analyzed. We do so at the end of this section. In the mean time, we put forward a brief history of biodiversity databanks, which is part of the history of cyber-infrastructures  Bastow and Leonelli (2010)  have analyzed. These authors rightly emphasize that "databases and other online resources have become a central tool for biological research". Hereby, we present some historical elements relative to biodiversity databanks and emphasize their specific international features.
A Brief History of Biodiversity Databanks Less than 10 years after the ratification of the Convention on Biological Diversity (CBD), 4  several databanks were created, whose main objective was to collect geographic and taxonomic data. Among these, we will focus our discussion on the ones relative to the marine field. They are the result of intense collective work during the 1990s aiming at the standardization and organization of data types. We present the development of this collective work in Fig. 3. 1. Figure  3. 2 shows its results. The very first databanks have been devoted to taxonomical classification, like the Integrated Taxonomic Information System (ITIS), itself derived from the National Oceanographic Data Center, a former databank from US NOAA (National Oceanographic and Atmospheric Administration).
The Taxonomic Database Working Group (TDWG), which first worked on plants, then had an important role in the elaboration of data standards, as well as the bioinformatics working group called "Global Initiative Taxonomy" (OECD 1999). The Global Initiative Taxonomy was the first step toward the creation of the GBIF databank, a few years later  (Wieczorek et al. 2012). Let us now review some outputs of the collective work aimed at the constitution of biodiversity databanks. First, it must be emphasized that the taxonomic impediment (here presented in all its urgent details: https://www. cbd. int/gti/problem. shtml) is a crucial problem facing the development of biodiversity databanks: taxonomists are too few and too slow to cope with the urgency of the biodiversity crisis and do not manage to catch up with extinction rates, thus leaving many extinct species unnamed and un-described.
In the 1990s, in the same period in which standard-ization of biodiversity data was occurring, Bionet International (https://www. uia. org/s/or/en/1100052951), a technical cooperative network for taxonomy, was launched in order to foster exchanges of taxonomic knowledge among different countries and help facing the taxonomic impediment. This was the pre-condition for developing unified knowledge by trying to overcome the heterogeneity of taxonomic expertise among countries. Against this historical background, it is important to emphasize that biodiversity knowledge relies on the development of genetic as well as taxonomy-based databanks. The most important genetic databank is GenBank (see below). It is com- monly used as a resource for biodiversity knowledge and assessment even though its primary goal is not biodiversity-oriented.
By contrast, BoLD (Barcoding of Life Databank) focuses on the link between genetic sequences and taxonomic practices. It aims at being part of a genetic tool for quick species identification. BoLD, and its marine component MarBol, complement traditional taxonomy-oriented databanks like Ocean Biogeographic Information System (OBIS, http://www. iobis. org/), World Register of Marine Species (WoRMS, http://www. marinespecies. org/), and Encyclopedia of Life (EOL, http://eol. org/), which is not restricted to marine organisms. These databanks have been fueled by data gathered during the Census of Marine Life project  (CoML 2000 (CoML -2010) )  aimed at collecting data and providing researchers with bioinformatics tools. This was an international project gathering 2700 scientists who collected taxonomic, geographic, and ecological data from 540 oceanographic expeditions.
At the beginning, databanks focused on taxonomic classification and upgrading thereof, but they soon had to face a new challenge: connecting taxonomic data with data coming from other kinds of classifications, like genetic, biological and ecological classifications. For instance, biological classifications deliver data like attributes of life stages, reproduction, body size, behavior, feeding method, and diet  (Costello et al. 2015). Establishing easy-to-retrieve connections among taxonomic data and biological or ecological traits is an important means to providing conservation biologists with indicators of the not-well-being of ecosystems (e. g. , impact of pollution, of fishery, of climate change). To do so, some traits in ecosystems (like reproduction rates and features of habitats) must be described and named on a standardized basis.
This cannot be achieved unless a robust consensus has been reached within the scientific community of both ecologists and taxonomists. Marine Species Traits (http:// www. marinespecies. org/traits/) aims at the generation of these traits from taxonomic (WoRMS) and geographic databanks (OBIS), which requires and important work of coordination and terminological standardization, as emphasized by  Costello et al. (2015). The latter recall that "a rich terminology surrounds descriptions of a species biology and ecology, with sometimes different definitions for the same terms, synonymous terms, and context dependent (e. g. , habitat) terms. This terminology has developed over several hundred years of natural history, in different languages, and often terms have multiple meanings in common use. " This requires databanks' designers to perform scientifically-informed terminological regimentation.
Biodiversity Cyber-Infrastructures How do the biodiversity databanks we have mentioned so far play contribute to the development of biodiversity knowledge? First, they make data easily accessible. Second, they allow for data being interoperable in a sense to be discussed below. More generally, they organize the vast amounts of data that are relevant to biodiversity study. We discuss and illustrate this three aspects in the following. Let us begin with accessibility of data. As already emphasized by  Leonelli (2010) , accessibility of data through internet-based databanks is a major precondition for knowledge-building. However, common accessibility, namely, the capacity of a piece of data to be easily retrieved at any scientific institution in the world, without overcoming outrageous paywalls, is not enough to define a useful biodiversity databank.
Common accessibility is just the baseline condition of any useful scientific databank. We define scientific "usefulness" in this context as the capacity of databanks to facilitate and accelerate the work of researchers and increase the validity of their results. This cannot only be done by gathering data; organizing data of various origins and nature is instrumental. This involves links among, not only within databanks. Let us emphasize what organizing data and data flows means. Biodiversity databanks, considered collectively, are not as efficient as they could be when information is scattered and when data come from heterogeneous origins. A good biodiversity databank thus should provide researchers with a unique entry to a variety of types of data, e. g. , genetic and taxonomic data from various geographical zones.
We may illustrate this point with sampling-event data (https://www. gbif. org/sampling-event-data). They report the presence of an organism of such or such species (usually rare or endangered, but not always) together with its spatiotemporal location. These taxonomic, geographical, and temporal data may be provided by amateurs or professional taxonomists. Multiple programmes contribute to produce sampling-event data but they do so for different purposes, some of which might be related with conservation efforts, others with the development of ecological models or the study of migrations and the effects of climate change. This heterogeneity of purpose may generate confusion as to how these data should be stored and used. A scientifically useful sampling-event databank (in our sense) should organize access to information and information flow in such a way as to diminish heterogeneity of origin and facilitate integration of data into more structured pieces of knowledge.
We now turn to interoperability, which can be defined as "the ability of two or more systems or components to exchange information and to use the information that has been exchanged"  (Covitz 2004 , quoted by Leonelli 2013a). The increased number of databanks involved the ratification of data standards in order to facilitate interoperability among databanks. This allows any individual databank to function as data provider for other databanks, thus facilitating accessibility. The major requirement for interoperability is that databank designers organize data along common rules: in 2009, the Darwin Core version, which defines minimum standard data (with glossary and synonymy) related with biodiversity, has been internationally adopted for that purpose. To sum up, accessibility, organization of information, and interoperability contribute to improving the usefulness of biodiversity databanks.
What Are Data in Biodiversity Databanks? Databanks are not just (organized) collections of data, but also participate in the very definition of what may count as data in the knowledge of biodiversity, a point already made by  Leonelli (2013b)  about the field of plant science. As emphasized above, different elements are called "data" in biodiversity databanks, like gene sequences, species descriptions, taxonomic revisions, and geographical information. What do all these elements have in common? For what reasons can they be defined as "data" and qualify for being included in databanks? We shall present and discuss the following working hypothesis about data in biodiversity databanks: what they have in common is not that they are all basic or fundamental in the same sense (this would correspond to a definition of "data" as intrinsically basic pieces of information), but that they may play the same epistemic role: they can be relied upon in the further steps of a scientific inquiry.
This functional way of understanding data may be contrasted, in both cases, with the view according to which data are defined by their simplicity or easiness of acquisition. Neither gene sequences nor species descriptions are simple or easy to acquire. They are both issued from complex processes. First, a gene sequence is the result of an interpretative judgment with respect to the result of a biochemical experience; the judgement is about which nucleotides appear in the sequence and their order. Second, when a gene sequence is integrated in a databank, for instance in BoLD, where it is associated with a species name (which can be temporary), researchers may also provide the file containing information about the relevant genetic material, the Polymerase Chain Reaction (PCR) primers used to generate the sequenced amplicon, the identifier of the specimen, as well as the collection record, i.
e. , the location of the original specimen in a collection of natural history. These items may help other scientists check whether the sequence has been associated with the right species name. They illustrate how complex the transformation of a gene sequence into useful data is. On the side of species description, it should be emphasized that associating a specimen with a species name is also the result of a complex process of hypothesis assessment. This association itself possesses a hypothetical status, as it can be changed (via taxonomic revision) when a new set of characters is taken into account or when new specimens are collected. The information-processing facilities that are currently operated within databanks allow databank designers to create links between a specimen and the various taxonomic hypotheses (species names) that have been associated with it over time.
In the marine field, this revision process can be followed in the literature and more easily in the WoRMS databank (see below). The above shows that biodiversity data, the components of biodiversity databanks, are not called "data" because of their simplicity or because they are easily obtained. They are bona fide data thanks to the robust scientific processes on which their production relies. This means that even though some of them require complex material devices for their generation or years of scientific education, these processes are judged reliable enough to be bracketed as the inquiry develops. To put it in another way, the components of biodiversity databanks are so firmly established that, even though they do have a hypothetical character, as any item of knowledge within empirical science, this hypothetical character can be ignored as far as we know.
For sure, they do not have the same status as proven mathematical theorems, but they are sufficiently well established to count as firm grounds for knowledge production. For all the above-presented reasons: international effort of standardization, accessibility, interoperability, robust processes of data production, databanks appear as an efficient way to overcome the discrepancy between the reality of biodiversity knowledge, which is heterogeneous and patchy, and the hope that it may become more and more homogeneous and united. In the next part, we present how existing biodiversity databanks are used by scientists in order to make clear in which ways the organizational logic of databanks relates to the dynamics of knowledge development.
Uses of Biodiversity Databanks This section is devoted to studying the various ways scientists, as distinguished from conservation practitioners and policy makers, use biodiversity databanks in order to develop biodiversity knowledge. We try to disclose the requirements useful databanks have to fulfill and how the data they include are operated in the production of knowledge. In Sect. 3. 3. 1, we describe, based on examples, what scientists do with the data they retrieve from databanks as well as the quick evolution of this scientific practice. In Sect. 3. 3. 2, we systematically compare catalogs and databanks in order to explore the specificities of the relationships between a databank and its expected users. At last, we try to show the underlying organizational principles of biodiversity databanks and how they may foster the evolution of scientific knowledge.
What Do Scientists Do with the Data They Retrieve from Biodiversity Databanks? As explained above, "data" in biodiversity databanks are already complex units of knowledge that are used to work out other types of scientific results, usually more general and systematic. These data are used to express general hypotheses that cannot be formulated unless two important features of data in biodiversity databanks are realized: they have (i) to cover large geographical regions or large taxonomic groups and (ii) to be valid. Scientists interested in biodiversity study, assessment or conservation may be familiar with a taxonomic group or geographical area and have personal estimates of species abundance in this group or of biodiversity in this area; however, they cannot only rely on their personal experience to put forward general hypotheses and submit them to empirical test.
Usually, personal experience, however valuable, is not robust enough to allow for hypothesis testing. By contrast, data in databanks possess the quality that personal connection with biodiversity will always lack: they have been validated by the scientific community, and as such, as explained in Sect. 3. 2, they can be relied upon to explore new, more general hypotheses and build up quantitative models. In order to illustrate, first, how databanks provide researchers with valid pieces of knowledge that they can rely upon and second, the quick transformations of this practice, we shall now present a recent databank that has been created at the Paris Muséum National d'Histoire Naturelle. It is called "BasExp": Databasis for scientific Expeditions (https://expeditions. mnhn. fr/) and has been designed to gather data related to a 40-year-long programme of marine expeditions initiated by Paris Muséum National d'Histoire Naturelle and the Institut de Recherche pour le Développement, first called "Musorstom", then "Tropical Deep Sea Benthos".
We now turn to the context in which BasExp has been created. This will shed more light on its purpose and potential benefits for researchers. Most first-generation biodiversity databanks have emerged as answers to inventory requirements. The practice of biodiversity inventory is characterized by its being static in the sense that it is blind, by nature, to changes in biodiversity. Inventories may now be considered of limited interest for biodiversity knowledge because changes in biodiversity are currently a major epistemic challenge, either with respect to conservation or in order to assess the effects of climate change. By contrast with inventory-based databanks, more up-to-date databanks aim at tracking biodiversity change. The best way to do so is by connecting several databanks together in order to be able to follow the evolution of spatiotemporal data in as much details as possible.