dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Data sources. Data for the trophic parameters and data categories listed in Tables  1  and 2  were gathered from peer-reviewed scientific publications, grey literature (e. g. , agency reports, theses, and dissertations) and unpublished data by the authors of this paper. Data compilation on stomach contents, stable isotopes, FATM, and trophic positions, focussed on mesopelagic organisms, their potential prey and predators. For major and trace elements, energy density and estimates of diet proportions, our search concentrated on mesopelagic taxa. Nevertheless, we also gathered information from small or intermediate-sized epi-, bathy-or benthopelagic species found in the compiled data sources. These species were included because they play key roles in most marine ecosystems, both as important consumers of phytoplankton and zooplankton, and prey for many top predators, and can represent alternative energy pathways to mesopelagic organisms.
However, we stress that the data coverage for these species in the current version of the database is very incomplete. Our main interest was on data from the central and eastern North Atlantic, and the Mediterranean, corresponding to the study regions of the SUMMER project. When we could not find suitable data within this region, we extended the geographic scope of our literature search to the western North Atlantic. We did not search for datasets in open access repositories since those data can be easily accessed and extracted. However, some of the data provided by the authors of this paper have been previously deposited in PANGAEA. DNA sequencing-based methods, such as metabarcoding and direct shotgun sequencing, are emerging as promising tools in dietary analyses due to the high resolution in taxonomic identification of many prey simultaneously, and the potential to provide quantitative diet estimates from relative read abundance  29.
However, recent studies have shown that various methodological and biological factors can break the correlation between the number and abundance of ingested prey and the prey DNA present in the sample, and lead to biased estimates of taxonomic diversity and composition of diet  29, 30. Given the uncertainties remaining in the interpretation of DNA sequencing-based diet data, we decided not to include these data in MesopTroph until additional research demonstrates that these techniques can be confidently applied for quantitative diet assessment. We identified available data sources in the literature through systematic searches on Web of Science, Google Scholar, ResearchGate, and the Google search engine. We used multiple combinations of terms related to specific data categories (Table  3 ), in conjunction with the common or scientific taxon names (from genus to order), and the ocean basin.
For example, the search for stomach content data of fishes belonging to the family Myctophidae was undertaken using the following terms: "stomach content" OR "gut content" OR "prey composition" OR "diet composition", AND "mesopelagic fish" OR "myctophid" OR "Myctophiformes" OR "Myctophidae", AND "Atlantic" or "Mediterranean". For the mesopelagic and predator species known to be numerically abundant in the SUMMER study regions, we performed a second literature search using the common or scientific name of the species, along with the terms "diet", "feeding habits", "trophic ecology", "trophic markers", or "food web". We also examined the literature cited within each collected publication to locate additional data sources. We next screened the full text of the compiled studies and retained data sources that: (1) were collected within the region of interest, (2) reported quantitative data for the trophic parameters of interest, (3) reported the number of samples for pooled or aggregated data, and (4) provided sufficient details on the methodology to enable a quality check.
FATM Wet m Wet mass of the sample g Lipids/wm tot Lipid content of the sample expressed in percentage of total wet mass % C10:0, C12:0, C14:0, 14:1(n-5), C15:0, C16:0, 16:1(n-9), 16:1(n-7) (IUPAC: C16:1w7c), 16:1(n-5), 16:2, 16:2(n-6), 16:2(n-4), 16:2(n-3), 16:3, 16:3(n-4), 16:3(n-3), 16:4(n-1) %, 16:4(n-4), C17:0, 17:1(n-8), 17:1(n-7), C18:0, 18:1, 18:1(n-11), 18:1(n-9) (IUPAC: C18:1w9), 18:1(n-7) (IUPAC: C18:1w7), 18:1(n-5), 18:2(n-6), 18:2(n-4), 18:2(n-3), 18:3(n-6), 18:3(n-3), 18:4(n-4), 18:4(n-3),   Data extraction, cleaning, and formatting. We created a template table for each data category in Microsoft Excel to assemble all datasets into a single file, and to facilitate cleaning and standardization of data records. We added a large number of metadata fields to the tables to annotate details about the sampling (e.
g. , location, date, methods), sampled specimen(s) (e. g. , taxonomy, number and size of individuals, number of replicates, tissue analysed), and data source (e. g. , full reference, DOI) for every record. Data contributors formatted and incorporated their datasets directly into the tables. For published sources, the data and associated metadata were extracted manually or digitized from the article text, tables, or supplementary material into the tables. Extraneous or hidden characters, and values such as "NA" (Not Available) or "ND" (Not Determined), were deleted from the parameter and metadata fields. Measurements of trophic parameters were standardized to the same units (see Tables  1  and 2 ). Parameter values that were clearly incorrect (e. g. , δ 15 N > 20, or the frequency of occurrence of a prey higher than the number of stomachs sampled) were corrected by searching for the value within the data source.
When values could not be corrected, we deleted that data record. When available, we extracted information at the individual level. However, most studies reported data obtained from pooled samples of the same species. In some cases (e. g. , small specimens such as planktonic organisms), a minimum and maximum number of individuals in the sample was provided instead of the actual number of individuals sampled. We added two columns to the tables presenting the minimum and maximum number of individuals in the sample. By filtering the column "Ind No (maximum per sample)" for values >1, users can easily identify records with aggregated data and differentiate them from records where information was drawn from a single individual (i. e. , where "Ind No (maximum per sample)" =1). In addition, the tables Stomach contents and Estimates of diet proportions include a field "Sample ID" with a unique identifier of the sample.
Stomach contents. Stomach contents analysis is a standard dietary assessment method that potentially enables quantifying diet components with high taxonomic resolution  34. Three parameters are typically used to describe diet composition from stomach contents: the number of individuals of a prey type as a proportion of the total number of prey items (%N), the proportion of a prey item by weight or volume (%W), and the proportion of stomachs containing a particular prey item (i. e. , percent frequency of occurrence, %F)  35. When available, we collected data on the three parameters, as well as on the absolute number, weight, and frequency of occurrence of each prey type in the stomachs of each sampled individual or group of individuals. If stated in the data source, we indicate if prey weights were directly measured or reconstructed from hard remains (fish otoliths and vertebrae, cephalopod beaks), and if they represent dry or wet weight.
Some datasets contained records of prey items without corresponding weights or numbers. As a result, the cumulative percent of all prey items did not sum to 100%. This occurred in 11 data records for the cumulative %W, and nine for the cumulative %N. While we checked the accuracy of percentage values and adjusted rounding errors, we did not attempt to fill in missing values nor did we remove records with missing values. When prey values were reported by an upper bound (e. g. , "<0. 01"), we assigned a value of half of the upper bound to that record and the percent of all prey items in that sample were rescaled to add to 100. Prey types were recorded at the highest taxonomic resolution.
Stable isotopes. Bulk stable isotope ratios, primarily of carbon (δ 13 C) and nitrogen (δ 15 N), are increasingly used to examine predator-prey interactions and food web structure. Moreover, advances in isotopic mixing models allow the conversion of isotopic data into estimates of dietary contribution from different food sources  34, 36. We compiled δ 13 C and δ 15 N measured in specific tissues or in the whole body of individual organisms, or mean values (and standard deviations) for pooled samples. When available, the total organic carbon to nitrogen ratio (C/N) was also collected. Because lipids have more negative δ 13 C values relative to other major biochemical compounds in plant and animal tissues  37  , many studies correct for the lipid effect by extracting lipids from samples before analysis, or a posteriori, through mathematical corrections  38.
We searched the original data source to understand how the lipid effect was handled and separately report δ 13 C and C/N values for untreated and delipidized samples, as well as values that were mathematically-corrected. Major and trace elements. Organisms accumulate major and trace elements (including metals) directly from the external environment and/or indirectly through diet. As such, their elemental composition can help to infer dietary preferences, solve trophic links, and inform quantitative dietary analysis primarily based on stable isotopes or fatty acids  39, 40. We entered the individual concentrations (or mean concentrations, for samples with more than one individual) of all elements reported in a study into separate columns. We also collected the moisture content of the samples analysed (expressed in percentage).
A column indicates whether concentrations are expressed on a dry mass (weight of the animal tissue after being dried) or wet mass (weight of the animal tissue containing water) basis, to allow the conversion between both types of data if necessary (using the moisture percentage). energy density. Information on the energy density of prey is critical for estimating food requirements and consumption by predators, and modelling energy flux through food webs  41. We collected data from studies that measured energy density directly by bomb calorimetry methods, and from studies that measured the proximate composition (i. e. , the percentage of proteins, lipids and carbohydrates) of sampled tissues and converted these percentages into energy using combustion equivalents reported in the literature.
Fatty acid trophic markers (FatM). Fatty acid (FA) analyses have been long used to qualitatively describe resource use, by tracing distinctive FA signatures of organisms into the lipids of consumers. More recently, quantitative fatty acid signature analysis and Bayesian models are emerging as powerful techniques to estimate the proportional contribution of different resources to consumer's diet  34. The FATM table compiles the proportion of each FA measured in sampled tissues or in the whole body of organisms in relation to total FAs analysed. For pooled samples, we collected the mean and standard deviation (when available) of the percentage FA. In all data records, we included the full range of FAs with values above 0. 1% but excluded FAs that were measured together. Care should be taken, however, since these percentages are not absolute but are affected by the number and particular mix of FAs analysed in each study  42.
The most reported elements are Cadmium (66%), Zinc (63%), Copper (58%), and Iron (49%). Similarly, each data record in the FATM table include the proportion of 8-36 fatty acids (average of 19. 4), the most common being palmitic, stearic, DHA, EPA, and oleic acids, available for >97% of records. Overall, 66% of all data records included in the current version of MesopTroph are drawn from single individuals but these percentages varied greatly across datasets (Stomach contents, 90%; FATM, 76%; Stable Isotopes, 71%; Trophic positions, 54%; Major and trace elements, 13%; Energy density and Estimates of diet proportions, 0%). MesopTroph is available for download at the World Data Center PANGAEA. A parent dataset (https://doi. org/10. 1594/PANGAEA. 946020  50 ) merges seven datasets, that can be downloaded separately as tab-delimited files, or in a single ZIP file:   The parent dataset (https://doi.
org/10. 1594/PANGAEA. 946020  50 ) also provides access to a data dictionary, listing the fields in all datasets, along with their description, format, and units (when applicable). The first row of each dataset contains the abbreviated name of the parameters, and the first column contains a unique record identifier (Record number). The datasets provide information on the location, dates and method of sample collection, taxonomic ranks (phylum, class, order, family), number and size (or size range) of sampled organisms, method/model used in data analysis. All published data records include the full citation and persistent identifier of the data source and data (when available). For unpublished data records, the name and affiliation of researchers that provided the data are given.
technical Validation Most unpublished data included in MesopTroph were provided by authors of this paper, who are experts on the organisms and techniques used. Data extracted from the literature underwent a basic quality check by verifying the appropriateness of the sampling and analytical methods in the original publication. After entry into the database, several manual and automated checks were performed to ensure that the entered data were correct. This included checking for missing information, duplicate entries, typos, spelling errors or inconsistencies. Values of all trophic parameters were visually evaluated to detect values far outside the expected range for that parameter. Clear outliers were verified and corrected by comparing with the original data source. When the outlier was not the result of a transcription error and was as reported in the data source, we either deleted the value for that specific parameter, or removed the corresponding record.
Usage Notes MesopTroph is available for download from PANGAEA as a compressed folder containing the tab-delimited files for all data categories and a data dictionary text file, or as separate files. Data are released under the CC-BY licence, meaning that there are no restrictions on its use, but it should be appropriately referenced by citing the present paper. If a single or a few specific datasets are used separately from the database, we encourage users to cite the original data source in addition to MesopTroph database. The database will be updated in the next few years with data from the literature, and raw data and analytical outputs emerging from the SUMMER project. Expected additions include expanding the taxonomic and geographic coverage of the database, and increasing the sample size with data from recent years.
52 ) ach contents -stomach content records, presented as frequency of occurrence, numbers or weight of a food item (https://doi. org/10. 1594/PANGAEA. 946139 51 ; 2. Stable Isotopes -isotope ratios of carbon (δ 13 C) and nitrogen (δ 15 N) (https://doi. org/10. 1594/PANGAEA. 94591752 ).
3. Major and trace elements -concentrations of essential and trace elements (https://doi. org/10. 1594/ PANGAEA. 945907 53 ); 4. Energy density -estimates of energy density of total body mass of organisms determined by calorimetric methods (https://doi. org/10. 1594/PANGAEA. 945870 54 ); 5. FATM -fatty acid profiles, expressed as percent of total fatty acids (https://doi. org/10. 1594/ PANGAEA. 945881 55 ); 6. Trophic positions -estimates of the trophic position of organisms calculated from the analyses of stomach contents or stable isotopes (https://doi. org/10. 1594/PANGAEA. 945968 56 ); 7. Estimates_Diet proportions -estimates of the mean proportional contribution (±standard deviation) of a food item to the diet of a sample of predators calculated from analyses of biochemical tracers (https://doi.
acknowledgements Data have been made available through the  EU  funded project  SUMMER  ( H2020-BG-2018-2 , proposal number:  817806-2 ). MAS and SPJ were supported by  SUMMER  ( H2020-BG-2018-2 , proposal number:  817806-2 ). MAS and AC were supported by the  OP AZORES2020 , through the Fund  01-0145-FEDER-1279 000140  " MarAZ Researchers: Consolidate a body of researchers in Marine Sciences in the Azores " of the  European Union. AC was also supported by  FCT -Foundation for Science and Technology  through project  CEECIND/00101/2021. AB was supported by  SUMMER  ( H2020-BG-2018 , proposal number:  817806 ). MPO and AB acknowledge the support of their institution through the  'Severo Ochoa Centre of Excellence ' accreditation ( CEX2019-000928-S ). JS and TC were supported by  SUMMER  ( H2020-BG-2018 , proposal number:  817806 ) and additional financial backing was provided by the  French Ministry  in charge of the environment.
TS was supported by the  US National Science Foundation. The A. P. Sloan Foundation and the  Institute of Marine Research, Norway , supported the MAR-ECO sampling effort. Okeanos is funded by  FCT  under the project  UIDB/05634/2020  and  UIDP/05634/2020 , and by the  Regional Government of the Azores  through the initiative to support the  Research Centers of the University of the Azores  and through project  M1. 1. A/REEQ. CIENTÍFICO UI&D/2021/010. We are grateful to  Astrid Wittmann  for the final check of the database.
1. 1 52 ) ach contents -stomach content records, presented as frequency of occurrence, numbers or weight of a food item (https://doi. org/10. 1594/PANGAEA. 946139 51 ; 2. Stable Isotopes -isotope ratios of carbon (δ 13 C) and nitrogen (δ 15 N) (https://doi. org/10. 1594/PANGAEA. 94591752 ).
INTRODUCTION Graphs are natural abstractions for representing interconnected data. They have been adopted in a large array of applications, ranging from social networks to scientific datasets, fraud detection, recommendation systems, and the Semantic Web. Their most expressive underlying data model is the property graph one, along with its numerous variants  [1, 3, 10] , in which lists of properties are attached to both the nodes and the edges of a directed, labeled, multi-graph. Property graphs underlie several open-source and commercial systems, out of which graph databases are the most prominent. While these do not impose a priori rigid schema constraints, the rich property graph model enables them to compactly capture complex patterns, while remaining digestible and self-explainable for end users  [9].
However, the lack of a schema hinders several applications, such as data exploration, query formulation and optimization, meta-data management, and the extraction of type-based graph features for various machine learning pipelines. Moreover, property graph schemas are considered as relevant abstractions needed in future graph processing systems  [10]. To address this, in DiscoPG, we tackle the discovery of property graph schemas, leveraging a hierarchical clustering algorithm, based on Gaussian Mixture Models. The hierarchical clustering method based on GMM has been shown  [2]  to be tailored to the complex property graph data model. Compared to previous schema discovery methods  [7] , DiscoPG allows to account for both labeling and property information and to build node clusters, reflecting the underlying typing hierarchy of the base dataset.
Ours is a purely statistical approach (presented in detail in  [2] ), which we have experimentally shown to provide better accuracy and performance than previous analytical methods  [7]. While schema discovery methods have been proposed for RDF datasets  [4] , notably based on distributed clustering using Spark, our method is centralized and supports the more expressive property graph model. DiscoPG allows users to engage in the following scenarios: • visualization and exploration of the discovered schema, with novel cluster-centered layouts that showcase the size of the discovered node types, their corresponding labels and properties, as well as those of their connecting edges. • interactive inspection of a schema dashboard, displaying the performance of the underlying hierarchical clustering algorithms and the quality of the obtained schema, across each iteration of the discovery process; this allows analyzing the runtime behavior of the various methods and underpinnings of the graph clustering process.
• analysis of the property graph schema evolution, with custom rendering of the parts affected by modifications to the base graph instance. Both incremental and recomputationbased schema evolution scenarios are considered. We showcase DiscoPG on both real-world and benchmarking datasets, e. g. , CovidGraph  [6]  and LDBC  [5]. These have different characteristics and range from simple to complex schemas. The implementation of DiscoPG is available on Github. Given a graph dataset G, DiscoPG implements novel static and dynamic algorithms for discovering the underlying schema of G.
If there is an overlap of property keys between all sub-clusters, this intersection set is assigned to    , as it represents the "core" properties of. C H is then updated to record that the base types of the sub-clusters extend. For each sub-cluster, the procedure is re-iterated, as new reference nodes are computed and used to discover new sub-types. The key feature of the schema discovery algorithms of DiscoPG, allowing to capture both node labels and properties at the same time, is that they enable users to obtain more accurate schemas than previous approaches  [7]. 2. 1. 2 Schema Discovery for Evolving Graphs. DiscoPG also supports schema discovery in the dynamic case, wherein the dataset G evolves, upon batch inserting a set of nodes Δ. We outline the novel I-GMM-D and GMM-D methods, tailored to this end.
System Architecture As depicted in Figure  1 , the architecture of the DiscoPG system integrates three modules. These support discovering and exploring dynamic property graph schemas, as well as inspecting and logging the performance of the underlying algorithms and the quality of the produced results, through a dashboard. We present each below. • Schema Discovery. The first functionality of the module is that it allows to preprocess a given graph dataset and standardise its labels and property keys, by removing potential syntactical inconsistencies and typos. It then applies the GMM-S algorithm to discover the underlying schema structure of the graph obtained as output during the initial step. Finally, upon receiving updates from the user and a preference regarding whether to apply an incremental (I-GMM-D) or a memoization-based recomputation (GMM-D) approach, it computes a novel schema, reflecting the changes.
• Schema Exploration. The module allows users to navigate the previously produced schema graph. In particular, they can examine the labels and properties associated to both its nodes and edges. Moreover, they can immediately visually grasp the proportion of node instances corresponding to each node type, as these are reflected by the depicted cluster sizes. In the dynamic case, the individual impact of the changes to the relevant clusters can also be apprehended through their custom color coding. • Schema Dashboard. Finally, users can inspect the performance of DiscoPG's algorithms, as well as the quality of its produced schemas. As an additional functionality, providing a wider view of the computed metrics, the module enables users to log the results obtained with various parameter configurations, across several datasets.