dataesr/scientific-paragraphs-categorization huggingface dataset:
FAIRness assessment Evaluation results for 13 maturity indicators of the available datasets are presented in Table  2  (papers on molecular epidemiology) and Table  3  (other papers). The assessment criteria are described in the Methods section (Table  4 ). A number of papers provided the data used in the study within the manuscript itself (text or tables) (Fig. 2 ), including 4 and 10 papers on salmonids and dairy cattle, respectively. Those datasets were not evaluated for FAIRness as they do not form a distinct digital resource from the article itself. Molecular digital resources were almost always identified by a globally unique identifier defined by the repository, but this identifier was generally not persistent (F1). Most resources were associated with metadata including the data identifier (F2 and F3) and provenance information (R1.
2). All resources were indexed by a search engine (F4) and accessible via an open, free protocol (A1) but the persistence of the metadata should the resource become unavailable was not guaranteed (A2). No resources used a formal knowledge representation language, but all were provided in standardized formats, such as GenBank data (I1). Most used FAIR vocabularies (I2) and about half were linked to other relevant resources (I3). Clear license conditions were not often provided (R1. 1), given that several common repositories such as GenBank state that the repository managers "cannot provide comment or unrestricted permission concerning the use of the information contained in the molecular databases" or similar. All repositories but one were certified or listed as trusted in known community schemes (R1.
3). The digital resources evaluated here originated from a range of 12 individual countries and from two multi-country studies. Within non-molecular datasets, most digital resources were identified by a globally unique and persistent identifier, generally a Digital Object Identifier (F1). Six out of nine resources were associated with metadata including the data identifier (F2 and F3) while most had • In manuscript 0 4 1 0 • As supplementary materials 0 3 0 • In an online repository 24 5 1 Number of datasets included in the FAIRness assessment 30 8 1 Fig. 3  Presence of a data availability statement in the journals (N = 62) publishing the selected articles according to publication year and species. Some individual journals may appear in more than 1 year, species or discipline. Note that 2020 publications were assessed until October 18th only provenance information (R1.
2) and were indexed in a search engine (F4). Accessibility indicators were similar to those observed above for molecular data (A1, A2). No resources used a formal knowledge representation language and only two were provided in editable, nonproprietary format (I1). No resources used standardized vocabularies, and only two provided some documentation regarding the vocabularies used (I2). None were linked to other relevant resources (I3). Clear license conditions were always provided (R1. 1) and five datasets were deposited in repositories certified or listed as trusted in known community schemes (R1. 3). The nine digital resources which were evaluated originated from a range of countries: Canada (two datasets), Finland (one dataset), Mexico (one dataset), Norway (two datasets) and USA (one dataset) or from multi-country studies (two datasets).
Finally, the data sources for the nine non-molecular epidemiological digital resources evaluated in this section were databases from government agencies (e. g. , the Directorate of Fisheries in Norway  [50]  and the Aquatic Health Committee of Oaxaca in Mexico  [49] ) or international agencies (e. g. , European Community Reference Laboratory for Fish Diseases  [48] ), data from the published literature or data specifically generated for the purpose of the study. The dataset made available by Soler-Jiménez et al. [54]  in their literature review was the only dataset from a commercial source. However, this dataset was not collected specifically for the purpose of the study described in this publication. The authors were able to obtain mortality, environmental and management data from a group of fish producers in Mexico in the context of another study, which could not be identified in peerreviewed sources at the time of writing.
Discussion Most datasets used in non-molecular epidemiological studies were not findable, or "re-useless" as characterised The scores correspond to whether a given maturity indicator was met (2), partially met (1) and not met (0), respectively (cf. Table  4 ). The mention NE corresponds to a resource which could not be evaluated given that the dataset accession number was invalid. Datasets which have obtained the same combination of scores on the 13 indicators are grouped in the same row. Given that six source articles had two datasets associated with them (deposited in separate repositories), their reference appears twice in this table. To distinguish them, the corresponding repository is indicated in brackets after the reference. 2 2 0 0 2 2 0 0 0 0 2 0 0 [ 46] 2 2 2 2 2 2 2 1 1 0 2 2 2 [ 47]* 0 0 0 0 0 2 0 0 0 0 2 2 0 [ 48] 2 2 2 2 2 2 2 0 0 0 2 2 2 [ 49] 2 2 2 2 2 2 0 0 1 0 2 2 2 [ 50] 2 0 2 2 2 2 0 0 1 0 2 2 0 [ 51] 2 2 2 2 2 2 2 0 0 0 2 2 2 [ 52] 2 2 2 2 2 2 0 1 0 0 2 2 2 [ 53] 0 0 0 0 0 2 0 0 0 0 2 0 0 [ 54] The scores correspond to whether a given maturity indicator was met (2), partially met (1) and not met (0), respectively (cf.
Such considerations also apply to the data shared in supplementary files assessed in this study. For supplementary materials, metadata may not be readily available (F2), although researchers may manually find relevant information in the article, and these resources sometimes do not have a unique resource identifier (F1), although some journals associated a dedicated DOI to each supplementary item. Supplementary materials were not searchable in search engines, as the article itself is the object that is indexed (F4). Although providing raw data as supplementary materials does not make datasets FAIR, it is a common and practical way for researchers to respond to the increasingly pressing requests to make data available. It could be argued that there is little difference between sharing raw data in a manuscript table or in a supplementary material table and that treating them differently is arbitrary.
However, a criterion for what constitutes raw data had to be established for the specific purpose of this study. In accordance with the FAIR framework which focuses on digital objects, we chose to only assess the FAIRness of raw data which were provided as separate digital resources, as described in the Methods section. While the number of papers for which the raw data were available was similar between salmonid and dairy cattle in non-molecular epidemiology (12 and 11, out of 61, respectively), datasets were made available as individual digital resources more often by salmonid researchers than by dairy researchers (8 and 1 datasets, respectively). It was not possible to compare the FAIR maturity indicators between the two species given the small sample size and they are discussed here together.
Across disciplines and without considering the case of supplementary material datasets which was already discussed above, the FAIRness assessment showed that most resources were uniquely identified, although persistence of these identifiers was generally not provided for molecular datasets (F1). Indicators F2, F3 and R1. 2 were often met, with some metadata provided for the digital resource, along with the data identifier and provenance information. The persistence of the metadata should the data objects be removed was generally not guaranteed regardless of the repository considered. Importantly, all data objects were discoverable by web-based search engines (F4) and freely accessible via an open protocol (A1). While the available objects generally scored favourably for Findable, Accessible and Reusable indicators, Interoperability was more problematic.
The datasets identified in our study were not using a "formal, accessible, shared, and broadly applicable language for knowledge representation" as per the definition of the I1 indicator  [1]. In non-molecular epidemiology, datasets were often shared as Excel files, with little to no documentation of the content of the different data fields, and no linked objects could be identified. Much progress remains possible on the I1, I2 and I3 indicators for these resources. Examples of existing resources which may be used to improve Interoperability in epidemiological research are the AGROVOC vocabulary developed and managed by the Food and Agriculture Organization (http://www. fao. org/agrovoc/) and the SNOVET systematized nomenclature for veterinary medicine  [55]. By contrast, standardized formats (e.
g. , XML) and vocabularies are used in the National Centre for Biotechnology Information (NCBI)'s databases considered in this study for molecular datasets (GenBank and Sequence Read Archive) (23 out of 30 datasets). As the I1 maturity indicator is not associated with an agreed list of what constitutes a valid language for knowledge representation for a given discipline, there is room for interpretation by the person performing the assessment in terms of defining their scoring system. As such, the scoring system used in the present study may not be valid for another study or discipline. It is important to recognize that, as the coding of molecular data is universal, it is much easier to achieve interoperability for such datasets, as shown by the good scores achieved on this indicator to non-molecular epidemiology.
In addition, the digital archiving of such data is concentrated by a few stakeholders as NCBI is collaborating with other large repositories such as DNA DataBank of Japan and the European Nucleotide Archive, making it practical for the interested researcher to find related data, even if they are not explicitly linked (I3). Molecular epidemiology papers were also frequently providing epidemiological data (isolate or sample data) in a tabular format (70% of papers). Such data, which can be considered as raw data or as metadata for the genetic data, are critical to support reusability. Although inferior to the availability of genetic data (80%), the availability of epidemiological data was substantially higher than that observed in nonmolecular papers. Finally, frequent re-use of published genetic sequences by other researchers is observed in the literature, showing the progress made in this discipline compared with other disciplines.
In summary, the relatively consistent structure of molecular datasets, the availability of appropriate repositories, and the existing demand for data re-use are some of the factors which may explain the differences in data availability observed between molecular and non-molecular papers in our study. Meeting indicators related to the Findable, Accessible and Reusable principles is likely possible for researchers who do not have specific skills in data management. On the other hand, interoperability appears to be a more complex objective. The process of migrating the Pathogen-Host Interaction Database, in plant sciences, to a FAIR-compliant form  [56]  illustrates that the data transformation required to apply machine-readable standards for knowledge representation require specialist knowledge in this area.
This may be an obstacle for both funders and scientists who may not readily understand the concepts or have the skills required for effective data preparation, management and long-term preservation  [57] [58] [59]. In this regard, more systematic data management training is needed in graduate programmes, both to develop awareness around open science and FAIRness and to teach specific skills required to reach these goals. Meanwhile, researchers may already take simple measures to increase the interoperability of their datasets. For example, documenting the content of tabular data in a systematic manner, indicating the content, type and unit of each data field, is an accessible step for rendering datasets self-descriptive, short of using knowledge representation languages and FAIR vocabularies.
Units and conditions of measurement are critical for the re-use of quantitative data. In the case of 'oxygen' levels in fish cages, one needs to know the type and unit of the measurement (e. g. , oxygen saturation in %, oxygen concentration in mg/L) as well as the depth and temperature of the measurement to allow for correct interpretation. No comparable assessments of FAIRness in publications were found in the literature but the subjects of data sharing and open data in research have been abundantly documented. A strong contrast is observed between willingness to share research data expressed by scientists and availability of datasets in practice. Survey respondents often declare willingness to share at least some data publicly (around 80% of them depending on the discipline and study)  [60, 61].
Recent studies looking at the proportion of published work for which datasets were publicly available showed relatively low levels of availability: 8% in geoscience flux research  [62] , less than 10% in psychology research  [57] , 14% in morphology research  [63]  and 18% in biomedical research  [64]. Even in biomedical journals with a full data sharing policy for randomized controlled trials, a review found that only 17 out of 37 eligible articles satisfied the definition for data availability  [65]. Our present findings are consistent with these observations. Despite the plethora of published literature on the topic of data sharing and the growing availability of technological solutions, the evolution of practices and attitudes remains slow. In this regard, as our study was conducted only 4 years after the principles were formally published, it is not surprising that no significant changes were observed yet.
These results will provide a baseline measure for future evaluations aiming at identifying longer-term changes. Regardless, some scientific disciplines are significantly ahead in terms of data sharing, for example in genomics  [66] , as confirmed in the present study for molecular epidemiology in animal species. In other disciplines, barriers to making research datasets available have been explored  [57, 61, 67, 68]  and some of the factors which can positively impact data sharing have been identified  [69] [70] [71]. Top-down pressure, in the form of strong encouragement or policies of mandatory sharing, from funding bodies and journal publishers appears to be highly effective. For measurable progress, such policies and requirements must not remain theoretical but need to be verified in practice.
Data management plans indicating how FAIR principles will be applied are an increasingly common requirement from research funding organisations  [72, 73]. In the animal production sector in particular, data provided for research by stakeholders may be considered as confidential information, due to animal health and production data revealing production practices as well as representing commercial assets and competitive advantages. Fourteen of the 122 non-molecular epidemiology papers reviewed in this study were based on commercial data, including four in dairy cattle and ten in salmonids. The availability of raw data was very low for these potentially sensitive production data (only one of the fourteen papers made the raw data publicly available). It is reasonable to assume that raw data provided by commercial producers are even more concerned by privacy issues than datasets collected by other means.
In such cases, researchers are subjected to conflicting needs, with transparency, reproducibility and reusability on one side, and data confidentiality on the other side. Our results suggest that researchers may have more room to make datasets available when they collected these data from third-party sources, such as government databases holding data submitted by industry for regulatory purposes (for example the study by Myksvoll et al. [50] ). Sourcing research data from third-party data integration initiatives may allow the generation of epidemiological datasets which are easier to share publicly, due to pre-existing data sharing agreements with the industry. An example of such initiative found during this review is Fish-iTrends, a sea lice data management system administered by the Atlantic Veterinary College in Canada  [74].
Another example in salmonids is the attempt to set up a data integration platform described by Meyer et al. [75]. The use of animal production commercial data for research can significantly increase access to quality data with excellent coverage in time and space of the populations of interest, as shown by studies included in this work such as analyses conducted in the Chile salmonid industry  [76, 77]  and in the Canadian dairy industry  [78]. The confidentiality level required for these datasets as well as barriers to data sharing related to competitiveness and anti-trust regulation aspects suggest that there could be a fundamental incompatibility between the principles of open-access data and the use of commercial data for research. However, progress towards more FAIRness does not require the datasets to be made open access.
The four first levels presented by Mons et al. [2]  are compatible with access-restricted datasets. Data integration systems such as those proposed above may allow progress to be made towards improved FAIRness. For example, authors making datasets findable and their metadata findable, accessible, interoperable and reusable would reach the fourth of these levels, "FAIR data with restricted access". This would better enable other researchers to assess which digital resources could potentially be useful to them and express their interest in reusing such datasets. Negotiations regarding the conditions for access, such as the appropriate type of license, could then be undertaken between interested parties. This would represent significant progress from the "reuseless data" stage where most of the datasets assessed in the present study were found.
Finally, our assessment showed that molecular datasets appear relatively compliant with machine readability, while this was generally not the case for the nine non-molecular available datasets. Thus, machine readability remains uncommon for non-molecular epidemiological data, especially as these nine datasets only made up for a small proportion of the 61 studies initially identified. The approach proposed by Wilkinson et al. [12]  puts emphasis on machine readability: "detecting and validating behaviours of digital objects that make them machine-readable and reusable". Given the relative novelty of FAIRness implementation in veterinary epidemiology, it is not surprising that only few resources were standardized for machine readability, while most provide human-readable content. In addition, the FAIRness assessments strictly focus on the digital object itself, and therefore do not assess other aspects of compliance with generic data management good practices, such as data curation and governance.
The salmonid data platforms used in some of the studies  [74, 76, 77]  show the progress made by producers and data users to explore new ways of managing data, which require data curation and governance aspects to be defined in a collaborative manner. The FAIR principles also do not consider discipline-specific attributes which may be considered critical. For instance, some authors proposed an extension of the FAIR principles to better address the reproducibility and privacy protection challenges encountered in health research  [79]. There are no standard indicators available to assess the additional aspects, and they were not considered within the scope of this study. Last, FAIRness assessments may also be applied to model code objects, when these are shared by their authors. Although we did not specifically address this aspect in the present work, we noted that none of the fourteen mathematical modelling studies provided access to such model objects.
Conclusion In conclusion, we recommend that the FAIR framework is progressively integrated in the routine workflow of researchers in veterinary epidemiology, starting with more education, training and communication. Furthermore, the barriers to reach the goals of data re-usability which have been set for a few years should be identified by discipline. This would allow the design and implementation of interventions to overcome these barriers. Last, we suggest a stepwise approach to improving the FAIRness of research data, in which the first step would be to make a large proportion of datasets and their metadata findable as digital resources.
Methods The methods used for this systematic review are reported in accordance with the PRISMA statement  [80]. A literature search was conducted on October 18th, 2020 to identify all peer-reviewed publications related to epidemiological research in salmonid The indicator identifiers are the same as the corresponding guiding principle identifiers for simplicity production and in dairy cattle published between January 1st, 2017 to October 2020. We searched scientific literature referenced in three electronic databases: Scopus, Web of Science and PubMed. The documents retrieved were imported into a desktop reference management system for screening, eligibility assessment and further analysis. For the salmonid dataset, we used the following keywords: (epidemiology) AND (salmon OR trout) AND (aquaculture).
All titles, abstracts and key words were screened to select the results relevant to our study, i. e. , articles related to epidemiological research in salmonid production. All farmed salmonid species were included (e. g. , Atlantic salmon, coho salmon, rainbow trout). We excluded (i) studies in other disciplines (clinical reports, molecular biology, proteomics, bacteriology, microbiology, parasitology, 1  phylogenetics, physiology, food safety, economics, welfare), (ii) studies not conducted in farmed salmonids (wild salmonids, other species) and (iii) documents other than peer-reviewed publications (e. g. , conference papers). Three additional articles were removed at the full-text review stage (see below) as they were discussion papers and therefore not based on any formal dataset. For the dairy cattle datasets, we used the following keywords: (epidemiology) AND (dairy) AND (cattle OR cow).
Given the large number of database results, only titles and key words were screened to select the results relevant to our study, i. e. , articles related to epidemiological research in dairy cattle. Abstracts were reviewed only when the title and key words did not provide sufficient information. Exclusion criteria (i) and (iii) mentioned above were used. For criterion (ii), studies not conducted in dairy cattle (e. g. , dairy goats) were excluded. Additional exclusion criteria were (iv) articles which were not available in English and (v) molecular epidemiology papers. The latter were not considered for inclusion, as the comparison between salmonids and dairy cattle focused on studies in non-molecular epidemiology. Given the large number of eligible results for dairy (739 articles), we used random sampling to select full-text articles which were reviewed for data availability.
The sampling was stratified by publication year (2017 to 2020), with the number of dairy papers selected each year matching the number of available salmonid papers for that year. A pseudo-random number generation function in Microsoft Excel was used for this purpose. The full text of all articles selected for inclusion was then reviewed and assessed, along with any supplementary materials and information available on the publisher's Web page for the article. Relevant information for our study (DOI, publication year, species, country, title, type of work and information regarding nature and availability of datasets) was extracted and tabulated. The nature of the data used in the study was classified as commercial (i. e. , data collected and managed by commercial producers for their own purposes) or non-commercial data (i.