dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We are grateful to  Florian Winkler ,  Heinrich Aukenthaler ,  Erhard Seehauser , and  Gottfried Hopfgartner  (Forestry and Hunting Authorities South Tyrol, or  Jagdrevier Mauls ,  Bolzano Province, Italy)  for their support in our study of Alpine marmot biology in their wild habitats of Mauls and Gsies (Italy). Further, we thank  Dorothee Huchon  ( Department of Zoology, Tel Aviv University, Israel ) for help in rodent phylogenies; Love Dal en ( Swedish Museum of Natural History, Sweden ),  Nicolas Bierne , and  Aylwyn Scally  ( University of Cambridge ) for key discussions related to the genomics part of the manuscript; and  Mark Wilson  ( The Francis Crick Institute, UK ) for help with parasite defense genes. We are grateful to  Kerstin Lindblad-Toh  and the  Broad Institute (MA, USA) vertebrate genome team  for providing the genome sequence of the thirteen-lined ground squirrel.
We further thank  Jenny Barna  ( University of Cambridge, UK ) for help with software tools,  Y. Yuan  as well as the  European Molecular Biology Laboratory (EMBL) IT core facility  and for managing highperformance computing resources, and  Bogoljub Trickovic  for help with mining the microsatellite database. Further, we thank  M. L. Travert  ( France ) for providing photographs of wild-living Alpine marmot in the La Grande Sassiere  National Park (France)  (Figure  1A ). This work was supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001134 ), the  UK Medical Research Council  ( FC001134 ), and the  Wellcome Trust  ( FC001134 ). C. B. and A. C. are supported by the  Agence Nationale de la Recherche  (project  ANR-13-JSV7-0005 ) and the  Centre National de la Recherche Scientifique (CNRS) , and C.
B. is supported by the  Rhone-Alpes region  (grant  15. 005146. 01 ). L. D. is supported by  Agence Nationale de la Recherche  (project  ANR-12-ADAP-0009 ). T. I. G. is supported by a  Leverhulme Early Career Fellowship (grant   ECF-2015-453 ) and an  NERC grant  ( NE/ N013832/1 ). J. M. G. is supported by a  Hertha Firnberg Fellowship  ( FWF T703 ). L. D. R. is supported by the  Diabetes UK RD Lawrence Fellowship  ( 16/ 0005382 ).
Exposome-Explorer (http://exposome-explorer. iarc. fr) is a database of dietary and pollutant biomarkers measured in population studies. In its first release, Exposome-Explorer contained comprehensive information on 692 biomarkers of dietary and pollution exposures extracted from the analysis of 480 peerreviewed publications. Today, Exposome-Explorer has been further expanded and contains a total of 908 biomarkers. Two additional types of information have been collected. First, 185 candidate dietary biomarkers having 403 associations with food intake (as measured by metabolomic studies) have been identified and added. Second, 1356 associations between dietary biomarkers and cancer risk in epidemiological studies, which were collected from 313 publications, have also been added to the database.
Indeed, the idea of a cancer 'exposome' is gaining increasing traction among cancer epidemiologists. The exposome refers to all external agents to which the human body is exposed, from conception onward. Key to exposome studies is the assumption that specific chemical biomarkers can act as surrogates of exposure that provide more direct, unbiased and objective measures of environmental exposures than possible with traditional methods  (4, 5). The Exposome-Explorer database was started in 2012 and its website officially launched in 2017  (6). The goal of Exposome-Explorer is to compile all available information on exposure biomarkers described in the scientific literature. The textual information found in scientific articles represents an important source of data but is intrinsically difficult to evaluate and interpret.
The objective of Exposome-Explorer is to identify biomarker information from peer-reviewed scientific publications, collect it, store it, and organize it, and finally make it more easily accessible to researchers. In the first release of Exposome-Explorer, data from 480 publications was manually collected for 692 dietary and pollutant biomarkers. This data included biomarker concentrations in biospecimens for different populations, temporal reproducibility, and correlations values between biomarkers and exposures. Chemical information, analytical methods, biospecimens and populations were also documented. Data was collected both for dietary and pollutant biomarkers, covering a wide range of dietary and chemical exposures. For the second release of Exposome-Explorer, we have placed an increased focus on dietary biomarkers.
In this release, the addition of two new types of dietary exposure information as well as important changes to the web interface are described. Firstly, a large number of candidate dietary biomarkers previously identified via metabolomics studies have been added. In particular, a number of novel biomarkers of exposure have been identified by comparing metabolomic profiles between exposed and non-exposed individuals. Secondly, more than 300 nutritional epidemiological studies that involved the measurement of nutrients in biological samples along with the usual food questionnaires were collated and analyzed for this year's release. Data on these dietary biomarkers and their associations with cancer risk were also added to the database. Expanding Exposome-Explorer with these new data sets and new data types has obviously increased its complexity.
Collection and compilation of new data New data on candidate dietary biomarkers identified in recent metabolomics studies, as well as associations of dietary biomarkers with cancer risk have been collected. Two sets of literature searches were conducted to identify relevant scientific papers. The first literature search for compiling dietary biomarkers with metabolomics studies relied on several reviews and papers  (7) (8) (9). This search resulted in the selection of 20 publications covering the years 2010-2016. The second literature search aimed at identifying scientific papers describing prospective epidemiological studies of associations between dietary biomarkers and cancer risk. This literature search mainly focused on vitamins, polyphenols and fatty acids. From this search 313 publications from 1984 to 2018 were selected.
Only peer-reviewed publications describing original work with biomarker measurements in human populations were considered. The same password-protected annotation interface as previously described for entering data in Exposome-Explorer was used  (6). New forms, fields and views dedicated to the collection of the new data were developed. The following information was collected: -Publication. and its bibliographical details. Study design was also indicated (e. g. 'nested case-control study', 'prospective cohort study' or 'case-cohort study' for cancer epidemiological studies). -Subjects. A short description of the subjects and populations, with country, cohort name, number of subjects and gender. For cancer epidemiological studies, the number of cases and the number of controls was indicated for nested case-control studies and the number of cases was indicated for prospective cohort studies.
g. Clustering, PCA). The methods used for the metabolite annotation as described in the publications are summarized in the 'Structural identification' field. For instance, if a match was obtained by comparison of spectral data with chemical standards, or from comparison with those in public databases and literature. Levels of confidence are used by some authors to standardize the reporting of metabolite identification across publications and studies  (10, 11). These levels can be found as well in the 'Structural identification' field. -Associations of dietary biomarkers with food intake (metabolomics studies). Significant positive associations between candidate dietary biomarkers and dietary exposures were inserted in the database. The type of food exposure was indicated. The method used for dietary assessment (e.
g. food frequency questionnaire) was indicated in the database. Only biomarkers with a name were listed. Statistics including AUC, sensitivity, specificity, PLS-DA-VIP, beta coefficient and its P-value, ANOVA and its Pvalue, and correlation values were indicated when provided. -Associations of dietary biomarkers with cancer risk. Significant and non-significant associations of dietary biomarkers and risk of 17 broad categories of cancers were recorded in the database.
Controlled vocabularies and their classifications Controlled vocabularies are used to describe compounds, foods, cancers, biospecimens and analytical methods. These vocabularies are organized using classifications. The structure of the MySQL database for the handling of the vocabularies and their classifications has been enhanced. Initially, classifications were stored in two fields (a group and a subgroup) of each controlled vocabulary table of the MySQL database. The consistency of the classifications was difficult to maintain and was error-prone. Classifications have been moved to a dedicated table where they are organized as a tree structure. The new classification system allows any number of levels and sub-levels inside the hierarchy and makes updates more flexible. Hierarchies of classification terms have been improved.
Database implementation Exposome-Explorer is a web application developed in Ruby on Rails (http://rubyonrails. org). The data is stored in a MySQL database (http://www. mysql. com/). Since our initial release, Rails and its gems have been regularly upgraded. Rails is now installed at version 4. 2. The jQuery DataTables plugin (http://www. datatables. net/) has been upgraded to version 1. 10. 15. Bootstrap (http://getbootstrap. com/) has been upgraded to 4. 0. 0. alpha6. The Wishart lab's Unearth gem used for the website global search has been replaced with Searchkick (https://github. com/ankane/ searchkick), also based on Elasticsearch indexing (https: //www. elastic. co/). Chemical structures are hosted on the the Wishart lab's MolDB structure server (http://moldb. wishartlab. com/). Chemical information (e.
Interface enhancements Exposome-Explorer's public user interface has been revamped in order to improve user's experience and to incorporate the new data types (Figure  1 ). In particular, the top navigation menu has been reorganized to improve clarity and navigational ease. The 'Biomarker data' menu gathers all the categories of data available in the database. These data include 'Biomarkers', 'Concentrations', 'Reproducibility', 'Correlations with exposures', 'Candidates from metabolomics' and 'Associations with cancer risk'. The 'Classifications' menu provides access to the controlled vocabularies and their classifications: 'Compounds', 'Foods', 'Cancers', 'Biospecimens', 'Analytical methods' and 'Cohorts'. Each classification is represented as a tree, which can be expanded or collapsed. Classifications are listed in a table below the tree.
This table can be filtered either by selection of one or several items inside the tree, or directly by typing a keyword in the filter box. With these changes it is now possible to retrieve all data available in the database for any level of classification (e. g. all data for 'Diet', 'Polyphenols' or 'Flavonols'). The 'Publications' menu lists all the annotated publications used to assemble the database. For each publication, numbers of the different types of data collected can be viewed when showing hidden columns. The 'Structure search' menu allows users to search by chemical structure or by molecular weight. From the 'Downloads' menu, users can easily find CSV files for several important Exposome-Explorer data sets. It is also possible to get the most current data, or data that is not available in the Downloads, with 'Copy displayed rows' available in any data table of the website.
Exposome-Explorer's quick search has also been made more effective. The dropdown menu has been removed and a typeahead functionality has been added. It is now possible to directly search for specific compounds, foods and cancers. Searches in the database are simultaneously executed on names, synonyms, classifications and CAS numbers. From typed keywords in the search box, matching results are proposed. While simply searching 'quer' or 'uerc' would return nothing in the former release, suggestions of matching terms from the database are now provided and the compound 'quercetin' can be selected. The page of results has been redesigned and name, classification, and numbers of data are displayed. Links to these data are provided. Scrollspy menus have been added at the top of the pages to more easily navigate the different types of information within the pages.
Menus and pages are now subdivided into 'Publications', 'Biomarker data' and 'Exposure data'. Biomarker data contains biomarker concentrations, biomarker reproducibility over time, associations of biomarkers with foods or dietary compounds, and associations of biomarkers with cancer risk. Exposure data contains dietary intakes and associations of exposures with biomarkers. A 'Chemical data' section has been added to the compound pages. This section shows 'MolDBI data' and 'ClassyFire Taxonomy' retrieved from the Wishart lab's servers as well as links to the original data on these servers. Finally, Exposome-Explorer's home page has been modernized and made more visually appealing and interesting. This is intended to make the website more appealing to firsttime visitors wishing to enter and discover the Exposome-Explorer database.
DISCUSSION AND CONCLUSIONS All of the data in Exposome-Explorer has been obtained from extensive primary literature analysis and manual annotation of scientific publications. In its first release, Exposome-Explorer contained data on biomarker concentrations, temporal reproducibility, and correlations between biomarkers and exposures. In this second release, new data on candidate dietary biomarkers identified via metabolomics studies and associations of biomarkers with cancer risk have been extracted and compiled from 332 publications. This led to the addition of 185 metabolomic biomarkers and 32 new epidemiological dietary biomarkers being added to Exposome-Explorer since the first release. The application of metabolomics to biomarker discovery in population studies is relatively new (the oldest publication in Exposome-Explorer is for the year 2010) and this explains the limited number of metabolomic publications added to the database.
However, this represents a large number of new candidate dietary biomarkers. Some of these biomarkers were added to the database despite uncomplete chemical information. For example, some ambiguity remains on the chemical structure of 'urolithin A glucuronide' which could either be a 3-glucuronide or a 8-glucuronide  (13). As they are still undetermined, such compounds were inserted with no chemical structure in the database. Nevertheless we hope that the presence of this type of data in Exposome-Explorer will encourage further characterization of these biomarkers. The field of exposomics and exposure biomarkers has developed together with molecular epidemiology. Biomarkers are used in molecular epidemiology to improve accuracy of exposure measurements or to provide exposure data that cannot be easily measured with questionnaires  (14, 15).
A first set of data was collected from prospective epidemiological studies of associations of dietary biomarkers with cancer risk. Users of the Exposome-Explorer database can discover which publications focused on a given cancer, which biomarkers were measured in these studies, and which biospecimens and populations were analyzed. For each publication, all investigated associations between biomarkers and cancer risk are listed. For more details on the statistical significance, association trends, or confounding adjustments, hyperlinks to the original publications are provided. Some of the most visible changes to Exposome-Explorer pertain to the improvements made in handling controlled vocabularies and their classifications. In our first release, it was only possible to browse food, dietary compound and biomarker classifications.
Now every vocabulary has its own hierarchical classification, which makes browsing more efficient and helps users to quickly locate and retrieve information from the database. For browsing chemicals, we have chosen to use our own exposure-based classification rather than the structure-based ChemOnt taxonomy. However, ChemOnt taxonomy is provided for each compound with a chemical structure. The purpose of our classification is to make search in the database for epidemiologists, nutritionists and environmental scientists, more intuitive to locate specific categories of chemicals and chemical exposures. We believe these additions significantly enrich our database and should make it even more useful for researchers interested in exposure or exposome studies. They should also help in developing new studies and new applications in this rapidly growing field of research.
5 ). Data sources. We used a database on cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration (NCD-RisC). NCD-RisC is a worldwide network of health researchers and practitioners, that systematically documents the worldwide trends and variations in risk factors for non-communicable diseases. The database was collated through multiple routes for identifying and accessing data. We accessed publicly available population-based measurement surveys-for example, Demographic and Health Surveys, Global School-based Student Health Surveys, the European Health Interview and Health Examination Surveys and those available via the Inter-University Consortium for Political and Social Research. We requested, through the World Health Organization (WHO) and its regional and country offices, help with identification and access to population-based surveys from ministries of health and other national health and statistical agencies.
Requests were also sent by the World Heart Federation to its national partners. We made similar requests to the co-authors of an earlier pooled analysis of cardiometabolic risk factors  [40] [41] [42] [43]  and invited them to reanalyse data from their studies and join NCD-RisC. Finally, to identify major sources not accessed through the above routes, we searched and reviewed published studies as described previously  44  and invited all eligible studies to join NCD-RisC. Anonymized individual record data from sources included in NCD-RisC were reanalysed according to a common protocol. Within each survey, we included participants aged 18 years and older who were not pregnant. We dropped participants with implausible BMI levels (defined as BMI < 10 kg m -2 or BMI > 80 kg m -2 ) or with implausible height or weight values (defined as height < 100 cm, height > 250 cm, weight < 12 kg or weight > 300 kg; <0.
To the extent that the reclassifications keep up with changes in the real status of each community, survey and population data reflect the status of each community at the time of measurement. For surveys without information on place of residence, we calculated age-and sex-stratified summary statistics for the entire sample, which represented the population-weighted sum of rural and urban means. To ensure summaries were prepared according to the study protocol, computer code was provided to NCD-RisC members who requested assistance. All submitted data were checked by at least two independent reviewers. Questions and clarifications were discussed with NCD-RisC members and resolved before data were incorporated into the database. Finally, we incorporated all nationally representative data from sources that were identified but not accessed through the above routes, by extracting summary statistics from published reports.
Data were also extracted for nine WHO STEPwise approach to Surveillance (STEPS) surveys, one Countrywide Integrated Non-communicable Diseases Intervention (CINDI) survey, and five sites of the WHO Multinational MONItoring of trends and determinants in CArdiovascular disease (MONICA) project that were not deposited in the MONICA Data Centre. Data were extracted from published reports only when reported by sex and in age groups no wider than 20 years. We also used data from a previous global data pooling study  43  when such data had not been accessed through the routes described. All NCD-RisC members are asked periodically to review the list of sources from their country, to suggest additional sources not in the database, and to verify that the included data meet the inclusion criteria listed below and are not duplicates.
The NCD-RisC database is continuously updated through this contact with NCD-RisC members. For this paper, we used data from the NCD-RisC database for years 1985 to 2017 and ages 18 years and older. A list of the data sources that we used in this analysis and their characteristics is provided in Supplementary Table  1. Data inclusion and exclusion. Data sources were included in the NCD-RisC database if: (1) measured data on height, weight, waist circumference or hip circumference were available; (2) study participants were 5 years of age and older; (3) data were collected using a probabilistic sampling method with a defined sampling frame; (4) data were from population samples at the national, sub-national (that is, covering one or more sub-national regions, more than three urban communities or more than five rural communities) or community level; and (5) data were from the countries and territories listed in Supplementary Table  2.
We used school-based data in countries, and in age-sex groups, with school enrolment of 70% or higher. We used data for which the sampling frame was health insurance schemes in countries in which at least 80% of the population were insured. Finally, we used data collected through general practice and primary care systems in high-income and central European countries with universal insurance, because contact with the primary care systems tends to be as good as or better than response rates for population-based surveys. Conversion of BMI prevalence metrics to mean BMI. In 2% of our data pointsmostly extracted from published reports or from a previous pooling analysis  43  mean BMI was not reported, but data were available for the prevalence of one or more BMI categories, for example, BMI ≥ 30 kg m -2.
National mean BMI was calculated as population-weighted averages of the rural and urban means. All analyses were done separately by sex because geographical and temporal patterns of BMI differ between men and women  2. The reported credible intervals represent the 2. 5th and the 97. 5th percentiles of the posterior distributions. We report the posterior probability that the estimated urban-rural BMI difference is a true difference in the same direction as the posterior mean estimate. We also report the posterior probability that the estimated change in the rural-urban BMI difference over time represents a true increase or decrease. Validation of statistical model. We calculated the difference between the posterior estimates from the model and data from national studies. Median errors were very close to zero (0.
All data-rich countries had at least one data source after 2000 and at least one source with data stratified on rural and urban place of residence. We fitted the model to the data from the remaining 90% of countries and made estimates of the held-out observations. In the second test, we assessed other patterns of missing data by holding out 10% of our data sources, again from a mix of data-rich, data-poor and average-data countries, as defined above. For a given country, we either held out a random one third of the country's data or all of the country's 2000-2017 data to determine, respectively, how well we filled in the gaps for countries with intermittent data and how well we estimated in countries without recent data. We fitted the model to the remaining 90% of the dataset and made estimates of the held-out observations.