dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors acknowledge the  CNRS program  " Physique et Chimie du Milieu Interstellaire " (PCMI) co-funded by the  Centre National d'Études Spatiales (CNES). A. C. is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  through  ERC Starting Grant "Chemtrip" (grant agreement  no  949278 ). The authors thank the IRAM staff, especially  Jan Martin Winters , for their help with the observations and calibration of the data. VW thanks  Fabrice Herpin  from the  Laboratoire d'astrophysique de Bordeaux  for interesting discussions on masers. The authors are grateful to the anonymous referee for the very helpful suggestions that improved the quality of the paper.
The Atlantic Zone contains only 20. 5% of the species' plausible continental range, but this zone contains all of the species' abundance series, nearly all of the genetic sampling locations used to evaluate the panmixia hypothesis, and most of the source locations for demographic parameters and environmental databases (Table  1 , Figure  1 ). The Atlantic Zone is also the source of nearly all (99. 7%) reported yellow and silver eel landings and most (68. 3%) reported glass eel and elver landings in 2000-2016  (Cairns, 2020). Stock assessments require data that represent the stock  (Righton et al. , 2021). Finding representative data for American eels should be easy because panmictic breeding systems produce genotypes that lack geographically based structure. However, the species' genetic system does not constrain variability but instead facilitates it by high plasticity of gene expression  (Côté et al.
| Use of species and environmental databases Natural resource information is increasingly being assembled into public databases with broad geographic scope  (LaDeau et al. , 2017). Many such databases include or potentially include American eel records (Table  2 ). The Global Biodiversity Information Facility (GBIF), Biodiversity Information Serving our Nation (BISON) and Ocean Biological Information System (OBIS) draw their information from scientific sources and some contain detailed metadata on individual records. The Aquatic eDNA Atlas accepts data gathered under standardised protocols  (Isaak et al. , 2018)  but has limited coverage within the American eel range. In addition, large amounts of data F I G U R E 1 Plausible historic continental range of the American eel, drawn from known current distributions up to watershed divides and to impassable natural obstacles.
Sources: Spawning zone-  Miller et al. (2015) ; abundance series locations and counts-ASMFC (2012, 2017),  Cornic et al. (2021) ; databases with coverage by zone-Table  1. All other data, including the range map, are from Cairns (2020) relevant to eel distributions, in particular most stream electrofishing series, remain web inaccessible. Where public databases are lacking, researchers have the option of building their own through data mining approaches (see next section). We identified 21 environmental databases that may assist American eel stock assessments (Table  1 ). Databases that map watershed boundaries are available for Canadian, US and world fresh waters. Soundings used for nautical charting are increasingly being made publicly available. The Canadian non-navigational bathymetry data set provides soundings at intervals as close as 10 m, although coverage and point spacing are variable.
The expanding use of lidar technology has facilitated high-resolution seamless mapping of land elevation and water depths in several stretches of the US East and Gulf of Mexico coasts. Databases also document key habitat parameters, such as gradient, temperature, flow characteristics and connectivity, of streams and rivers in the US and parts of eastern Canada (Table  1 ). The US National Wetlands Inventory, which covers open waters as well as vegetated wetlands, provides the most detailed aquatic habitat classification available in the American eel range (Table  S1 ) but contains no information on connectivity. The degree of shelter from the open sea strongly influences the distribution of many marine biota, including eels  (Cairns et al. , 2017; Pardal et al. , 2021). A database (Figure  S1 ) maps the degree of shelter as mean wind-adjusted fetch at 50 m resolution inshore and 200 m offshore for coastal and continental shelf waters of eastern North America.
Dams and other artificial barriers to stream and river flow are documented in databases with regional to worldwide scope (Table  1 ). However, databases concentrate on medium and large dams, leaving most of the several million small dams in the US uninventoried (Table  3  and Table S1 ). A newly developed machine learning algorithm, used to identify dams in Southeastern New York State, indicated that 80%-94% of dams were previously undocumented by any database  (Buchanan et al. , 2022). Small dam density in US Atlantic states, based on counts of 0. 5-40 ha ponds on 1:24,000 scale maps  (Fleming & Stubbs, 2012)  and the assumption that two-thirds of ponds are formed by dams (Renwick, 2017), is 0. 423 dams/km 2 (Table  S2 ). This dam density is 4. 3-19. 1 times higher than dam densities calculated from eastern US dam inventories that were created to guide the restoration of connectivity in river systems (Table  1 ).
This differential implies that dam inventories overlook most dams in river systems within their coverage areas. Crowd sourcing increasingly populates public databases. iNaturalist accepts species (including American eel) records from citizen scientists. Open Street Maps posts volunteer-supplied dam records, although coverage is sparse in the American eel plausible range (Table  1 , osm. org). In Europe, a consolidated barrier inventory containing ~640,000 records is believed to be missing many dams. Citizens are helping to add missing dams via a smartphone application  (Belletti et al. , 2020; amber. inter national). Crowd-sourced soundings were used to map the bathymetry of Newfoundland and Labrador shelves at higher spatial resolution than official charts  (Novaczek et al. , 2019). Where public databases fall short of needs, researchers have the option of building their own.
Using a data mining approach, American eel range edges in Mexico and the Caribbean Basin were clarified by searching records from museums, checklists and grey literature, followed by contact with local experts and in some cases museum visits to verify specimen identities  (Benchetrit & McCleave, 2016). This approach achieved the first reliable account of eel distribution in an area where formal ichthyofaunal surveys were rare. Similarly, early distributions of European eels on the Iberian Peninsula were mapped from historic records  (Clavero & Hermoso, 2015). Another data mining approach is to assemble quantitative survey data from dispersed sources, such as an electrofishing database for US mid-Atlantic states that was built by simply asking fisheries agencies for their records  (Lapointe et al.
, 2016). Similarly, data from 26 trawl and beach seine surveys were assembled to map detections and catch rates of American eels in marine waters between Labrador and Florida (Figure  S2 ;  Cairns et al. , 2017). Some input surveys were widely cited in peer-reviewed literature, whereas others were web invisible and only discovered by direct inquiries to data-holding agencies. Many additional surveys, not compiled in any database, may provide additional information on American eel marine distribution (Table  S3 ; Figure  S2 ). Organisations may be able to canvas potential data sources more thoroughly than individual researchers. For example, a consortium of private and public stakeholders in France, Spain and Portugal (SUDOANG) compiled a comprehensive set of eel-relevant data from heterogeneous databases  (Mateo et al.
LFA uses a training data set of lengths and ages to derive a function that estimates age structure from samples in which only length has been measured. LFA was used to estimate the age of eels ascending the Saunders ladder on the Moses-Saunders Dam on the upper St. Lawrence River, using the length and age of eels sampled during 2006-2008 for training  (Zhu et al. , 2013). Despite high variation F I G U R E 2 Mean growth rates of naturally migrating yellow and silver eels in the St. Lawrence Basin between recruitment to continental waters and the year of sampling, in relation to recruitment year. Data are from Table  S4  Legend Yellow eels Silver eels in length at age (Figure  S4 ), LFA successfully converted lengths to ages, enabling the calculation of cohort strengths for year classes between 1967 and 2005.
both major eel fisheries and extensive freshwater and marine data resources. A spatial database of stream connectivity and environmental parameters was assembled from the National Hydrography Dataset, StreamCat, the National Wetlands Inventory and the Freshwater Network barrier database (Table  1 ). Data sources used for the prioritisation of barrier removal showed a density of 0. 027 dams/km 2 in the Pennsylvania, Maryland and Virginia portions of the Chesapeake Bay watershed  (Martin, 2019a ) (Table  S2 ). Counts of small ponds from fine-scale maps  (Fleming & Stubbs, 2012)  and the assumption that two-thirds of ponds were formed by dams (Renwick, 2017) produced a density of 0. 345 dams/km 2 for these states. This density is 12. 5× higher than the density estimated by  Martin (2019a). The difference in these density estimates implies that most small dams in the region were not included in barrier databases.
For saline waters, the spatial database includes tidal and fetch mapping and bathymetry from a seamless topobathymetric elevation model (CoNED, Table  1 ). Most eel presence/absence, abundance and biological data are from electrofishing in streams and trawling in marine waters (Figure  5c ). Modelling aims to establish landscape-scale predictive relations between environmental drivers and eel distributions and population characteristics. This task is particularly complex in fresh water because eels present at a particular location depend on site characteristics, river connectivity chains and the history of barriers in the system. Machine learning approaches, including random forest and boosted regression trees  (Breiman, 2001; Elith et al. , 2008; Plant et al. , 2021) , accommodate the complexity of relations eel distribution and habitat attributes, with minimal requirements for distributional profiles of predictive variables.
To help guide longfin eel conservation and management, a modelling framework was founded on a digital network of 600,000 stream segments  (Snelder & Biggs, 2002) , with associated data on fish abundance, habitat characteristics and locations of dams  (Crow, 2017). Previous modelling efforts, based on machine learning methods, were static in time  (Crow et al. , 2014; Elith et al. , 2008). Here, an R program called Vector Autoregressive Spatio-Temporal (VAST) was introduced to treat both spatial and temporal variations simultaneously  (Thorson & Barnett, 2017). VAST uses delta-generalised linear mixed modelling (delta-GLMM) to estimate spatial response variation from locationspecific encounter, count and biomass data. Stream characteristics are spatially autocorrelated according to along-stream, rather than by straight-line distance.
" SPR-based BRPs have been calculated in this way for glass eel fisheries and silver eel turbine mortality in Nova Scotia (DFO, 2019). Spawner per recruit is an attractive modelling option for American eels because many input data are known or can be approximated from the literature. However, some aspects of the theory underlying SPR align poorly with American eel circumstances. SPR-based BRPs draw from the interlocking concepts of stock-recruitment (SR) relationships and maximum sustainable yield, in which a stock, diminished from its carrying capacity by fishing or other anthropogenic pressure, responds with a productivity increase that is a non-linear function of population size  (Russell & Potter, 2003; Zhou et al. , 2019). The nature and existence of SR relationships in anguillid eels are uncertain, because the alternative hypothesis, that recruitment variation is dominated by environmental factors rather than by adult population size, has not been disproven  (Fenske et al.
| Data Traditionally, fish biologists collect data on the distribution and abundance of their species of interest. However, with only a small cadre of eel researchers and a plausible historic continental range of 8. 5 million km 2 (Figure  1 ), eel-directed field collections cannot possibly meet assessment data requirements. Current public databases provide some eel records (Table  2 ), but no public databases systematically cover electrofishing, fishway passage or eDNA metabarcoding surveys, all of which detect eels. Data mining is an appropriate approach for obtaining data gathered by diverse methods from scattered sources  (Benchetrit & McCleave, 2016) , but fisheries data gathered at public expense using standard methods should be housed in public databases. Environmental databases, key enablers of spatially oriented modelling, have large gaps in eel-relevant data.
The widest of these is in dams. Most dams (~90%) in the conterminous US are not inventoried (Tables  1  and 3 , Tables  S1  and S2 ). The proportion of uninventoried dams in Canada and the Caribbean may be at least as high. Public posting of data on fish distribution and abundance, and broadening of environmental databases to include more features that impact aquatic animals (e. g. small dams), could aid the conservation of a wide range of aquatic fauna, including eels  (LaDeau et al. , 2017). In Europe, collaboration amongst diverse environmental stakeholders and widespread use of crowdsourcing have put needed data in the hands of eel biologists  (Belletti et al. , 2020; Drouineau et al. , 2021). The recent development of a machine learning algorithm that uses geospatial and lidar data to identify dams, even small ones, with reasonable accuracy (true positive rate 89%, false positive rate 1.
Data mining will bring to light useful information on American eel distribution and abundance, but the creation of new data, especially by eDNA, is required to define eel occupancy across its enormous plausible range. Spatial analysis methods could be a knowledge multiplier for eels, if relationships established in a study area can be used to estimate population and habitat use attributes in larger outside territories. However, gaps in environmental databases (notably non-coverage of small dams) constrain this potential. Bay/estuary, lake and especially small pond, habitat is underserved in anguillid eel status evaluations  (Briand et al. , 2022; ICES, 2009 ; Figure  5 ). Modelling that recognises density dilution by impoundment formation (Figure  4 ) can help clarify the effects of small dams on eels, but data on eel abundance in ponds, needed to implement this approach, are scant (but see Table  S5 ).
Theme Database Location Sal. a Comments Source nid. sec. usace. army. mil/ scien cebase. gov/catal og/item/56a7f 9dce4 b0b28 f1184 dabd 2c1fo rest. datab asin. org/galle ries/1b5e7 612ea d34f3 a9880 a6969 24bc91e/ fresh water netwo rk. org/proje cts/aquat ic- barri er-prior itiza tion/ conne ctivi ty. sarpd ata. com NID documents 92,071 dams, generally ≥7. 6 m high or impounding ≥61,674 m 3 of water. NABD maps NID dams on stream flowlines Stream barriers and potential barriers (4901 dams, 490 waterfalls, 114,797 road crossings) 13,889 records of dams 391,020 records of dams F F F F US SE Canada, NE US E US, ME to WV 25 states in central, S and SE US, Puerto Rico TA B L E 2 Species databases that show or potentially TA B L E 1 (Continued) Barriers National Inventory of Dams/National Anthropogenic Barrier Dataset Barriers Appalachian-Acadian Aquatic Connectivity Tool Barriers Freshwater Network Barrier Database Barriers Southeast Aquatic Barrier Database a Salinity: F, fresh; S, brackish or salt.
show American eel records Database Location Salinity a Number of American eel records Comments Source obis. org gbif. org bison. usgs. gov www. fs. fed. us/rm/boise/ AWAE/proje cts/eDNAt las/the-edna-atlas -resul ts. html Frimpong et al. (2016) inatu ralist. org Aggregated from numerous databases. Currently expanding to include eDNA records From research-grade sources. Currently expanding to include eDNA records Includes records from GBIF Crowd-sourced from researchers Compiled from fish atlases From citizen submissions Ocean Biodiversity Information System All F, S 15,590 (OBIS) Global Biodiversity Information Facility All F, S 9691 (GBIF) Biodiversity Information Serving our US F, S 12,163 Nation (BISON) Aquatic eDNA Atlas Western US, part of the F 3 Appalachian region IchthyMaps US F 3241 iNaturalist All F, S 1248 a F, freshwater; S, salt and brackish water.
eosc-life. eu/). The objective of the toolbox is to help researchers find resources that can help them make their sensitive data available for re-use. The content of the toolbox should cover existing recommendations, procedures, best practices, and links to software (tools) to support data sharing and re-use relevant for sensitive data management. In addition, guidelines and other useful resources drafted in the context of EOSC-life will be included. The toolbox demonstrator does not contain de novo information; instead, it helps scientists to navigate through previously collected best quality content available throughout the EOSC-Life collective infrastructure landscape. The design of the toolbox demonstrator and its subsequent population with content is designed to be driven by use cases, stemming from within EOSC-Life, to achieve our objectives and, work was and will be done in an iterative fashion using community approval processes  6.
Methods In the development of the categorisation system and the toolbox the LS RIs listed in Table  1  were involved. Table  1  also contains a short definition of RIs and a description of cluster projects. A concept description of the toolbox was provided at an early stage of the project  11. Categorisation system and pilot studies. Three different versions of the categorisation system were developed, each tested by a subsequent pilot study, and all published in Zenodo  [12] [13] [14]  , as illustrated in Fig. 1. To mitigate polysemic issues and to improve applicability and understandability, the different categories, and the individual tags of the categorisation system to be constructed were discussed in regular meetings of a multidisciplinary working group (WG) covering various RIs, and common definitions were adopted across RIs.
The re-tagging exercise with the second version of the categorisation system was performed for the same 110 resources (pilot study 2). This time only one expert from each of the 6 RIs was involved, selected from the experts involved in pilot study 1. The re-tagging (pilot study 2) was performed with a web-based software tool developed by ECRIN. The data were analysed with R to determine the variation of tagging between the RIs. The subsequent re-tagging exercise (pilot study 2) still highlighted practical shortcomings, which led to the decision to revise the categorisation system. Finally, the same resources were re-re-tagged (pilot study 3) with categorisation system version 3 by the same experts as involved in pilot study 2 and a similar analysis was performed. The concept behind the categorisation system is clarity and simplicity.
Results Categorisation system. The changes in the different versions of the categorisation system because of the evaluation studies are presented in Supplementary Table  S2  (Categories and pre-listed tags for the three different versions of the categorisation system). The initial version of the categorisation system covered 8 categories (resource type, research field, research design, data type, stage in data sharing life cycle, geographical scope, specific topics, perspective) with 93 pre-listed tags. The results of this pilot study 1 are published in  37  and summarised here. The inter-observer reliability analysis between the two experts from one RI demonstrated very high agreement for the two experts from BBMRI (median kappa for 25 resources: 0. 84). The kappa values were much lower for the other RIs with a range between 0.
The number of pre-listed tags for version 2 was reduced to 55. Categorisation system 2 was evaluated in pilot study 2 with the same 110 resources and one expert per RI. Apart from the tagging of the resources, additional feedback was received from five RIs, mainly related to the content and structure of the categorisation system. The category "data type" was seen as too complex and created major discussion. Several experts strongly suggested to better define this category and to make it easier to use. This input was accommodated in the next generation of the categorisation system (version 3). The "data type" dimension was clarified by the addition of a new "sensitive data type" category, listing the main types of sensitive data. This also allowed the number of tags in "specific topics" to be reduced.
Thus, categorisation system version 3 covers 7 categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, and specific topics with 65 tags (see Table  3 ). In the Zenodo publication of version 3 agreed definitions for the categories and tags are included  14. Categorisation system 3 was evaluated in pilot study 3 with the same 110 resources and the same experts as in the second exercise. For 1 resource there was missing data, so 109 resources could be analysed. Pilot study 3 was performed with a computer tool developed by ECRIN (SG). In general, the computer tagging tool was www. nature. com/scientificreports/ characterised as user-friendly and efficient, though some suggestions were made to improve functionality. One expert argued that the tagging should be done principally by one trained person or by a permanent small board, conceding however, that this may be too resource-intensive.
In this subgroup (n = 89), "any to type of data" was selected most often (n = 41). If resources were related to data not derived from living humans (n = 20), "any type of data" was also indicated most frequently (80%). For a considerable number of resources, "not applicable" (37%) was selected to characterise "stage in data sharing life-cycle". Most tags from the categories "geographical scope" and "specific points" were used at least 10 times. In addition, the number of resources tagged with only one tag per category was analysed and the results are presented in Fig. 3  for those categories that were considered in all three pilot studies. For this analysis and the initial tagging (pilot study 1) only those experts from the 6 RIs were included that also were involved later in the re-tagging and re-re-tagging exercise (pilot study 2 and 3).
This may also be important from the user perspective. At the end of the day, the system should be so intuitive that the users searching for terms would have the same logic as the experts entered the tags. To be beneficial for the domain of LS, the categorisation system and the toolbox requires broad community approval  38, 39. In the project, we began the approval process with nominated experts from 6 LS RIs, embedded in a larger working group of the H2020-funded project EOSC-Life, covering 13 LS RIs. Though this can be seen as a useful starting point, the toolbox obviously needs community approval at a much larger scale. As the categorisation system is specifying a part of essential metadata for resources about sensitive data, it will be relevant to the FAIR Digital Objects (FDO) Forum for a « resources in the life sciences » FDO.