dataesr/scientific-paragraphs-categorization huggingface dataset:
98 & p = 2. 1 × 10 -8 ), when considering data from the United States (i. e. , 44,488 taxa in 24,930,000 km 2 from 2,200,000 records;  [8] ). This result may also traduce the existence of a particularly efficient network of mycological associations developed (as compared to neighboring countries  [55] ) who share a taxonomically vast mycological expertise across a wide range of fungal lineages. Our analysis does not support this hypothesis, based on the results of a GLM procedure (Table  2 ). However, this absence of correlation between the density of the network of mycologists and the taxonomic knowledge recorded in the corresponding area may be considered with caution because of the intrinsic limits of both the database and the used method of analysis. Thus, 77 out of the 92 metropolitan departments do not accommodate any established contributor, while they all harbor fungal records in FongiBase.
The determinism is complex and may include sociological as well as ecological determinants. Based on citizen science contribution to large dataset, similar unbalanced recording density was previously reported for Fungi from Denmark  [6]. In a dataset assembling 292,022 records, these authors reported a negative correlation between human population density and mycological data density. In Fongibase, the network of mycological societies (i. e. , ADONIF, AMO, FMBDS, SMNF, ABMARS and CBNFC-ORI) provided the totality of records in three of the four clusters of mycological data (Figure  9  and Table  S4 ). Contrastingly to amateur mycologists, academic institutes such has the French Museum of National History and ADONIF highly contributes to the dataset (Figure  9  and Table  S4 ) but based on a highly diffuse way across the territory, by providing mycological data in all the 96 French departments.
Altogether, these results suggest that the patterns of mycological forays more than the location of their mycological societies shape the geography of data recording in France. This analysis also highlights the complementarity between the multiple contributors of FongiBase, including museums, educational institutions, mycological societies, and citizens to nourishing the National database. Complementary research, including sociological data and composite network analyses  [56] , is needed to provide a more accurate view of the links between mycologist recording activity, fungal diversity and landscape characteristics.
A Two-Century Long Dynamic of Fungal Data Recording Our analysis of the temporal dynamics of FongiBase revealed a four phase-pattern, with the major part of fungal observation being recorded since 1977 (Figure  3A, C ). The most ancient period extends from 1800 to 1886, and corresponds to a highly erratic pattern of fungal recording (Figure  3C ), with few records per year (41. 8 records per year), resulting in a modest contribution to the current database (0. 34% of the total number of records, for 3522 data). These records correspond to early contributions of particularly active mycologists (e. g. , L. Graves, which provided a significant contribution in 1857 in the Picardie department; Figure  S4 ). These historical records, and their related herbarium materials, are of primary importance for science as valuable resource for a wide variety of requests  [57].
During this period, fungal recording activity was fed by an unachieved network of mycological societies (Figure  3C ), with a main contribution of the Société Mycologique de France (SMF), the Fédération Mycologique et Botanique du Dauphiné-Savoie (FMBDS, created in 1960) the Association Mycologique de l'Ouest (AMO, created in 1952) and the Société Mycologique du Nord de la France (SMNF, created in 1967; Figure  3C ). From 1977 to 2007, a marked increasing of data recording emerged from the analysis, with 44. 4% of the total number of observed archived in Fongibase (Figure  3C ), corresponding to a recording intensity 37-fold higher (15,441. 7 records per year) as compared to the first part of the 20th century (i. e. , the second phase). This period coincides with the structuration of an extensive network of mycological associations, concomitantly with the progressive emergence of botanical conservatories as complementary contributors of FongiBase.
Interestingly, a similar temporal dynamic was reported by  [7] , who highlighted a high concentration of records since 1960 in the 6 million data assembled from Germany, the U. K. , Denmark, Norway, Austria, Switzerland, Estonia, Slovenia and the Netherlands observation networks. This part of FongiBase constitutes a highly valuable dataset for investigating mid-term change in macrofungal patterns at the scale of France. In particular, this corpus may be mobilized in order to feed red list initiatives as well as European global conservation initiatives  [61] , and large-scale assessments of the effect of global change on European fungal diversity  [18]. A majority (i. e. , 51. 6%) of fungal records assembled in Fongibase have been acquired since 2007 (Figure  3C ). In contrast with the period 1977-2007, the last 15 years did not show a progressive increasing in recording activity, but rather a stabilization followed by a sharp drop since the 2016 recording maximum  (60,275 and 33,178  records in 2016 and 2018, respectively; Figure  3C ).
A Deficit of Fungal Record in the French Mediterranean In this analysis, we identified a contrasted recording effort depending on the four ecoregions represented across metropolitan France, with a lower number and density of records per department in the Mediterranean as compared to Continental, Atlantic and Montane domains (Table  1 ). Interestingly, the Mediterranean shows a similar species richness as compared to other domains, but with a substantially lower sampling, ranging from 7. 1% and 22. 3% of the number of records gathered in the Atlantic and the Alpine domains, respectively (Table  1 ). Considered altogether, these results depict (1) the unbalanced sampling effort across French ecoregions, (2) the Mediterranean as a poorly represented area in the French fungal database and based on this deficit of records, as the potentially richest area for fungal diversity in France.
Beyond the Data: An Unbalanced Ecology of Records In Fongibase, our analysis revealed an unbalanced pattern of the representation of fungal guilds, with a marked dominance of mycorrhizal species in the database (Figure  8A ). This pattern reflects (1) the belonging of a majority of Agaricales, Russulales and Boletales to the ectomycorrhizal guild  [67]  and (2) the central place of forests dominated by ectomycorrhizal trees (i. e. , Fagaceae, Pinaceae, Betulaceae and Salicaceae) in the fields which are classically visited by French recorders (Figure  8A ), and more generally by mycologists during forays  [6]. This discrepancy among mycorrhizal and other fungal guilds was not confirmed at the species level. Thus, among the 10 most observed species, five (i. e. , Hypholoma fasciculare, Trametes versicolor, Stereum hirsutum, Pluteus cervinus and Xylaria hypoxylon) are wood decayers, while only four (i.
1% of assigned records), such as dry calcareous grasslands (3% of records) and in (2) artificial habitats (6. 4% of assigned records) with high sociological stakes, such as urban green spaces (4. 3% of records), cultivated landscapes (0. 1% of records) and cities/villages (0. 01%; Figure  8B ). The observed pattern may be explained by both a low level of interest in these habitats by amateur mycologists and by the presence of particularly poor communities of macrofungal species in these artificialized systems. The observed lack of ecological metadata associated with fungal records may be explained by the absence of a standardized procedure of fungal data recording, which may include a systematic characterization of the botanical context of the record. This characteristic of FongiBase limits its efficiency to provide a comprehensive overview of the real sampling effort devoted to mycological knowledge across the territory.
This author suggests that the observed lognormal distribution of large-scale data may be biased by the used citizen science approach, which favors repeated observations of easily identifiable species  [31]. Our analysis does not support this last hypothesis, as the FongiBase subdataset compiling fungal records from citizen science (i. e. , records from CBNFC-ORI, MNHN and RNF) fits a Pareto law distribution. Remarkably, our analysis shows that the number of singletons observed across the territory increases as the sampling effort increases (Figure  7B ), while the proportion of singletons concomitantly decreases (Figure  7A ). This relationship is significant, and follows a logarithmic model (Figure  7A, B ). This result suggests that the number of fungal species observed only once may be a good proxy of the quality of sampling effort, but the proportion of these rare species may inversely indicate the limit of knowledge of fungal communities at local scale.
This pattern may also suggest that the dominance of rarely observed species may be not artefactual, but in relation with the inherent characteristics of fungal communities at local scale. As a practical consequence of the above relationship between sampling effort and singleton patterns, the four clusters of fungal recording determined by our analysis appear as (1) the most efficient providers of rare species (Figure  6A ), as a consequence of the activity of a concentrated network of local associations (Figure  6A ) and (2) the regions where the proportion of rare species in the dataset is the lowest (Figure  6B ). When considering the wide range of contributors of FongiBase (Figure  9 ), a contrast between three types of contribution arises, based on the characteristics of the generated subdataset.
A first cluster of specialized mycologists, containing AscoFrance, SMF and SMRY, shows an ability to generate high-definition records in small datasets with many rare species (Figure  9 ). At the other extreme of the range, a group of highly active contributors, deploying citizen science methods across the territory, and containing CBNFC-ORI, RNF and MNHN, shows an ability to provide large datasets of frequent species (Figure  9 ). Located at an intermediate position between these two groups (Figure  9 ), a cluster of generalist mycologists, containing ADONIF, FMBDS, SMNF, AMO and UL, stands out by its ability to provide large dataset containing a high number of rare species, based on an intense sampling activity. These three types of fungal diversity observers, and the richness of the corpus they contributed to assemble in FongiBase, reflect the diversity of the French community of mycologists, and their complementary to synergistically feed large-scale initiatives.
Conclusions The heterogeneous nature of the dataset assembled by the variety of contributors of FongiBase, considered with the heterogeneous geography and ecology of data at both departmental and ecoregional scales, brings to light two conclusions. First, highly structured networks of mycological associations played a pivotal role in the French national mycological dataset by providing high-quality fungal records recoverable in the first regional red list initiatives. Second, there is a need to sustain the development of such organizations and help stimulate fungal recording activity in poorly documented areas, including the Mediterranean ecoregion and orphan habitats, such as agricultural landscapes, urban areas and open vegetation preceding forest establishment.
Attribution of four ecoregions in France by departments. The dotted line indicates the geographical limits of ecoregions without department administrative borders. The departments 75 (Paris), 92 (Hauts-de-Seine), 93 (Seine-Saint-Denis), and 94 (Val-de-Marne) are represented by a red star and they are assigned to the Atlantic ecoregion; Figure S2. Taxon accumulation curve for the four clusters of records in France. The color of curves indicates the corresponding ecoregion, with brown, blue and green for montane, Atlantic and continental domains, respectively; Figure S3. Distribution of taxa based on their occurrence in the database, ordered by observation classes using A. multiples of 100 records B. for taxa with less than 100 records (red bar in panel A), multiples of 10 records and C. for taxa with less than 10 records (red bar in panel B) detailed distribution per each frequency; Figure S4.
For example, when conflicting ideas emerged, we asked other participants their opinion on the topic. Different sources of data were collected as a means for comparison. Primary sources of qualitative data used for the analysis included observations, field notes, documents specific to the case study sites provided by participants (e. g. , brochures, reports, and mental maps drawn by interviewees), scientific papers, and the interview transcriptions, corrected and commented on by the participants. Participants gave formal written consent to write transcriptions of the interviews for further analysis of their content. Complete transcriptions were sent back to participants for content approval. To preserve participant confidentiality, their names were anonymized. Data management followed the ethics guiding principles of the author's research institution in respect of the European framework on data management (General Data Protection Regulation 2016/679).
Strengths Factor 1 Factor 2 Factor 3 Step 3 -Data analysis Step Successive iterations through the content of the interviews allowed for the extension and the adaptation of the initial codebook to each case study (second coding cycle). Mixed-method coding was used based on three coding methods: descriptive coding, In Vivo coding  (Saldaña, 2015) , and theme coding  (Miles et al. , 2019). A third coding cycle verified that codes had been consistently attributed within the datasets. Finally, the authors and co-authors reviewed the qualitative dataset to compare the findings. This analyst triangulation  (Patton, 1999)  aimed to limit potential biases when interpreting the data. Challenges and opportunities for the implementation of ESs were assessed using thematic analysis. PESTEL categories were separated into the four SWOT categories and synthesized into matrices where the categories were ranked based on the occurrence of each factor  (Bull et al.
, 2016). As the method combines SWOT and PESTEL analysis, the matrices present 24 quadrants instead of the classic fourquadrant analysis. Each row of the matrix corresponds to a PESTEL factor (6 rows), and columns correspond to the four SWOT factors. The PESTEL rows are organized following the distribution of the codes within the dataset, with the first row corresponding to the most representative PESTEL factor. Likewise, subfactors (e. g. , "Institutional Capacity" and "Climate change") are ordered into the matrix's cells according to their occurrence and SWOT type. For example, "Institutional Capacity" is an internal feature that can hence either be a strength or a weakness, depending on the case study, while "Climate Change" is an external feature that can either be an opportunity or a threat.
ACKNOWLEDGMENTS The authors thank  E. Cillaurren  for her help in identifying interviewees. Thanks to our  Move-On  project colleagues from the  University of Portsmouth ,  E. Trégarot  and  G. Ferraro  for their insights and guidance on this research. We also thank the project country coordinators who have facilitated the fieldwork ( R. Haroun ,  A. -L. Hérold ,  F. Otero ,  D. Strasberg , and  C. Villien ). Our special thanks go to the stakeholders participating in this study. We want to thank the constructive comments of two anonymous reviewers who helped improve the quality and relevance of the manuscript.
Purpose: To evaluate the cancer research effort of some major countries over two 5-year periods (2010-2014 and 2015-2019) on the basis of scientific publications and interventional clinical trial metrics and to analyze the relationship between research effort and cancer burden (incidence and mortality). Materials and Methods: Clinical trials were extracted from ClinicalTrials. gov using a specific query. Publications were identified in Web of Science (WoS) using a query based on keywords and were then analyzed using InCites, a bibliometric tool. Bibliometric indicators were computed per country and per period. Results: During 2010-2019, 1 120 821 cancer-related publications were identified in WoS, with 447 900 and 672 921 (+50%) articles respectively published in 2010-2014 and 2015-2019. Meanwhile, 38% and 7% of the articles were published in oncology and cell biology journals, respectively.
regard, there are many types of mortality depending on the affected organs, gender, socioeconomic factors, and geographic area. The economic impacts of cancer are also substantial, with global costs estimated at 1. 2 billion dollars in 2010. 4 dividual nations have established unique structures dedicated to the care of cancer patients. These structures have also resulted in numerous research programs targeted at cancer diagnosis and management. While many data are available on the prevalence, incidence, and mortality rate of cancer,  3, 5, 6  there is a lack of information concerning the research activities carried out in oncology. In 2019, approximately 60 000 articles were published in oncology journals across the world, thus putting oncology in eighth place among all disciplines and first among the medical disciplines (Web of Science (WoS)/Incites data).
In this regard, many robust databases are available for finding and retrieving scientific publications, with notable resources including PubMed, WoS (Clarivate Analytics), Scopus (Elsevier), and CrossRef. These databases are further reinforced by analytical tools such as InCites (Clarivate Analytics) and SciVal (Elsevier), which enable researchers to conduct bibliometric analyses on the basis of factors such as the specific discipline (e. g. , oncology, neurology), country of origin, and affiliated institution. However, from 1995 to 2019, only about 250 bibliometric studies related to cancer are shown in PubMed. These include studies describing the 50 or 100 most cited articles in a given cancer site or articles describing the evolution of the number of publications about a given cancer type.
These studies are generally limited to a single cancer  [7] [8] [9]  or to a single country of origin. 10 In fact, none of the available studies measured scientific output at the global level, by country of origin, or for all cancers combined. Addressing this gap is the objective of the current work. In order to guarantee the transparency of results, the International Committee of Medical Journal Editors requires that all clinical studies be declared in an international registry prior to initiation. 11 Here, researchers may use the ClinicalTrials. gov database, which was established in 2000,  12  specifically to declare the main parts of all relevant studies. As such, the database provides access to information on clinical trials initiated around the world, thus making it possible to measure research efforts in a given pathology based on the country of origin.
In this study, the main objective of gathering information on scientific publications and interventional clinical trials was to measure the cancer research efforts (quantitative and qualitative data) of different countries over two separate five-year periods, namely, 2010-2014 and 2015-2019. A five-year period is used in many bibliometric indicators and academic and research-related institution ranking, such as Shanghai Ranking  13  or SCImago. 14  searched the two following databases: • The WoS database and InCites platform, which provide volumes of publications per country and individual article impacts in terms of the number of received citations; • The ClinicalTrials database, which provides information related to clinical trial involvement. Analyses were first performed for all cancers combined and then by individual location while distinguishing between hematologic and solid tumors.
" We identified appropriate keywords for our searches by using terms available in the "Medical Subject Headings," a controlled vocabulary thesaurus used in Medline for indexing articles,  17  and by selecting those found in a sample of articles published in oncology journals. The resulting list was subsequently validated by two oncology experts, Pr. X. Troussard from CHU Caen Normandie  18, 19  and Pr. JP Metges from CHU de Brest. 20, 21 We then examined the contributions from major countries with reference to the global output, as determined based on the number of articles with authors from each country. We also compared the production of different countries with reference to their overall production in biomedical research (Medical & Health Science in the WoS database) and citation impact.
Here, a normalized bibliometric indicator compensates for the fact that the number of citations a given article receives is partly influenced by the year of publication (i. e. , older articles have more opportunities for citation) and size of the scientific field. 23 The CNCI of a given country is the mean CNCI of all articles co-authored by that country; the benchmark value is 1, with higher CNCI values reflecting higher impacts in terms of citation numbers. Next, we conducted our bibliometric analysis using the InCites analytical tool,  24  which was developed by Clarivate Analytics based on data indexed in the WoS. In this study, we only analyzed documents that were categorized as "Article" or "Review" in the WoS. We then extracted clinical trial data from ClinicalTrials. gov, specifically considering all interventional studies in cancer (Condition = Cancer) that were initiated from 2010 to 2019 across the world.
As our searches were based on specific keyword queries, the number of returned publications might not have been exhaustive. However, our objective was to compare different countries over two unique periods, in which case any potential bias should not imply a significant difference in rankings. In fact, the proportion of publications found in oncology journals (nearly 38%) confirmed that our decision to conduct a keyword analysis was appropriate. By querying the ClinicalTrials. gov database, we were also able to observe a rapid increase for China and Spain regarding their participation in cancer-related clinical trials.
| Relationship between the burden of cancer localization and research production Unfortunately, inclusion data are not available through ClinicalTrials. gov; the figures also presented centers without inclusion. Further, only interventional studies were included as many other types of studies (e. g. , registries or population-based studies) may be conducted without registration. The United States played a leading role in cancer research in the 10-year investigated period, accounting for around 30% of all related publications and 46% of clinical trials. However, major developments are evident in other countries over the same period. For example, China made significant progress, as shown by its SI (>2). In Japan, an and substantial provisions of dedicated resources. We also found a strong correlation between the number of registered trials and number of publications.