dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample collections We conducted a GWAS across nine discovery data sets (the majority overlapping with Genetic Consortium for AN, as part of the Wellcome Trust Case Control Consortium 3 (WTCCC3/WTCCC3 samples), resulting in a total of 2158 cases and 15 485 ancestrally matched controls (Table  1  and Figure  1 ). All AN cases were female. AN diagnosis was made via semistructured or structured interview, or population assessment strategy using Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV criteria for AN. The amenorrhea criterion was not applied, as this has been shown not to be diagnostically relevant  9  and has since been dropped from DSM-5. 10, 11 All cases met criteria for lifetime AN. Exclusion criteria included confounding medical diagnoses, for example, psychotic conditions, developmental delay or medical or neurological conditions causing weight loss.
Controlling for population stratification In order to account for population stratification, a principal components analysis was carried out for each cohort separately using the smartpca software. 24 opulation outliers were identified by merging each population with central European 1000 Genomes data. 25 ariance explained by each PC was plotted for each population. In order to be both conservative and consistent across populations, the first 10 principal components were included as covariates in the association testing.
Assigning variants to genes Variants identified associated at P ⩽ 1 × 10 -4 were assigned to genes using Ensembl (release 83; Ensembl Genome Browser). 29, 30 For each variant, all predicted consequences (for example, missense, non-synonymous, and so on) and associated gene transcripts were downloaded and compared. Each variant was associated with only one predicted consequence and one Ensembl gene ID (Ensembl Genome Browser). 29 uster plot checking Cluster plots were created for all SNPs reaching P ⩽ 1 × 10 -4 in any analysis (cohort-specific or meta-analysis) using ScatterShot. 31  SNPs were visually inspected for each cohort, and for cases and controls separately. In instances where multiple cohorts were merged (for example, UK cases), cluster plots were checked separately for each original cohort.
Pathway analysis One of the key motivations of studying complex psychiatric disorders such as AN is the desire to unearth biological pathways underlying disease development. Pathway analysis was performed using summary statistics from the meta-analysis for the full data set. Four pathway databases were used: the Kyoto Encyclopedia of Genes and Genomes (KEGG),  34, 35  the Reactome pathway database (REACTOME),  36  PANTHER pathway (PANTHER)  37, 38  and the Gene Ontology database (GO). 39, 40 These were curated to remove redundancy, resulting in a total set of 1836 pathways. The analysis was run once on a merged set of 235 KEGG, 34,35 REACTOME 36 and PANTHER  37, 38  pathways, and once for the 1601 GO pathways. 39, 40 athway analysis was carried out using MAGMA. 41 MAGMA was selected for its ability to deal robustly with linkage disequilibrium (LD) between markers, correct for gene length and deal accurately with rare variants.
Replication SNPs reaching P o1 × 10 -4 in the discovery stage were prioritized for replication. In total, 16 SNPs were selected. Replication was carried out using two data sets: one existing in silico data set and one set for de novo genotyping. The in silico data set came from an existing GWAS of AN,  7  genotyped on the Illumina HumanHap610 platform. This data set included 1033 cases and 3733 controls. All cases included in this study were female. Controls were both male and female. The de novo replication cohort consisted of 266 self-volunteered female UK cases, collected through the charity Charlotte's Helix (www. charlotteshelix. net). All participants were adults and had been diagnosed with AN by their clinician. In addition, all participants completed an online questionnaire based on the Structured Clinical Interview  43  for the Diagnostic and Statistical Manual of Mental Disorders-IV Module H.
The Structured Clinical Interview has been used extensively in epidemiological investigations. The Structured Clinical Interview eating disorder module was modified to capture information on lifetime history of eating disorders including AN, and includes questions on body mass index, age of onset, and experience of eating disorders. DNA from the saliva samples was extracted using standard protocols and was quantified using pico-green. Samples were genotyped on the Infinium HumanExome 12 Beadchip, genotypes were called using GenCall and Zcall algorithms and stringent QC was performed pre-and post-call. In all, 1500 ancestry-matched controls (55% female) were obtained from the UK Household Longitudinal Study. De novo genotyping was performed using the iPLEX Assay and the MassARRAY System (Agena Bioscience, San Diego, CA, USA) (formerly Sequenom).
Data availability Genotypes of European cases included in this study are publicly available through the European Genome-Phenome Archive (EGA), under accession number EGAS00001000913, data set EGAD00010001043, with the exception of German and Dutch genotypes. Genotypes for cases from the United States of America may be obtained through dbGaP. Summary statistics are available for download from the PGC website (https://www. med. unc. edu/ pgc/results-and-downloads).
ACKNOWLEDGMENTS Klarman Family Foundation : This study was supported by a grant from the  Klarman Family Foundation. Wellcome Trust Case Control Consortium: This work was funded by a grant from the  WTCCC3 WT088827/Z/09  entitled 'A genomewide association study of anorexia nervosa'. Acknowledgements for specific authors:  Eleftheria Zeggini  is supported by the  Wellcome Trust  ( 098051 ). Lorraine Southam is supported by the  Wellcome Trust  ( 098051 ). Laura Huckins acknowledges  Wellcome Trust  ( 098051 ) and the  MRC  ( MR/ J500355/1 ). Cynthia Bulik  acknowledges funding from the  Swedish Research Council  (VR Dnr:  538-2013-8864 ). Esther Walton  is supported by the  German Research Foundation  ( Wa 3635/1-1 ). Zeynep Yilmaz is funded by  NIH   K01MH109782. Allan Kaplan ,  Robert Levitan  and  James Kennedy  acknowledge support from  Ontario Mental Health Foundation  for the collection of samples.
Jaakko Kaprio is suppoted by the  Academy of Finland  ( 263278 ). Charlotte's Helix: We thank all the probands and parents from the parent-led group, Charlotte's Helix. This charity was set up by  Charlotte Bevan , after her daughter was diagnosed with anorexia nervosa. The charity is deeply committed to supporting biological work, particularly genetics, to help understand anorexia and has set up a database of patients' names and details. Charlotte's Helix has been collaborating closely with the King's College London (KCL) team since 2013 by providing the database of probands, some funding and publicizing the scientific projects through regular blogs on its website and social media. We gratefully acknowledge the participation of  NIHR BRC South London  and the  Maudsley NHS Foundation Trust (SLaM)  BioResource volunteers, and thank the  NIHR BRC SLaM BioResource centre  and staff for their contribution.
Prioritization of introduction pathways is seen as an important component of the management of biological invasions. We address whether established alien plants, mammals, freshwater fish and terrestrial invertebrates with known ecological impacts are associated with particular introduction pathways (release, escape, contaminant, stowaway, corridor and unaided). We used the information from the European alien species database DAISIE (www. europe-aliens. org) supplemented by the EASIN catalogue (European Alien Species Information Network), and expert knowledge. Plants introduced by the pathways release, corridor and unaided were disproportionately more likely to have ecological impacts than those introduced as contaminants. In contrast, impacts were not associated with particular introduction pathways for invertebrates, mammals or fish.
Introduction The management of individual introduction pathways, and corresponding vectors, of alien species is a potentially powerful strategy to prevent new species introductions and thus reduce both the future costs to society as well as negative impacts on biodiversity  (Carlton and Ruiz 2005 , Hulme 2009 , Essl et al. 2015). Pathway management is primarily aimed at eliminating or diminishing the propagule pressure of alien species and reflects the common wisdom that prevention and early action are more cost-effective than managing invaders after they have become established  (Leung et al. 2002, Kaiser and Burnett 2010). Information on the pathways of introduction is increasingly incorporated in alien species databases (e. g. IUCN ISSG Global Invasive Species Database, www. issg. org/database, CABI Invasive Species Compendium, www.
Data Data from DAISIE (  2009 ) database (www. europe-aliens. eu; Pergl et al. 2012 ) was used as a source of information on impacts of established alien species in Europe. It was also used as a basis for assignment of the pathways of their introduction to Europe for (i) vascular plants, (ii) freshwater fish, and (iii) mammals, while the EASIN catalogue (easin. jrc. ec. europa. eu; Katsanevakis et al. 2015)  was used for pathway and impact classification of (iv) terrestrial invertebrates. The classification of introduction pathways follows the scheme of  Hulme et al. (2008)  that allows their comparison across taxonomic groups as well as between accidental and intentional introductions. Each species was assigned to one or more of the following pathway categories: (i) release (intentional introduction and release into the environment), (ii) escape (intentionally introduced as a commodity, but escaped from culture), (iii) contaminant (unintentional introduction with specified commodity), (iv) stowaway (unintentionally introduced attached to or within a transport vector), (v) corridor (unintentional spread via human transport infrastructures linking previously unconnected regions) or (vi) unaided (unintentional introduction by natural dispersal across political borders following a primary human-mediated introduction in a neighbouring region).
The data do not differentiate between the pathways for initial introduction to Europe and those associated with movement among different European countries. Similarly, species are often listed as associated with more than one introduction pathway with no measure of their relative importance. In contrast to other taxonomic groups, the invertebrate data do not allow the exact area of origin to be identified for species that are native in a part of Europe and alien in another part and thus this group included only arrivals from other continents (classified as aliens to Europe in DAISIE 2009). Only species confirmed as established in at least one European country (DAISIE regions) were included in the analyses. As information on establishment status is incomplete for some regions of Europe, we also included species for which establishment could not be confirmed but that were found in five or more European regions.
Acknowledgements Contributions from  Vojtěch Jarošík  and  Francesca Gherardi  who both passed away during the work on the project are acknowledged with deep sadness. The study was supported by projects  SCIEX, DAISIE  and  COST TD1209  ( Alien Challenge ). JP, PP and IP were funded by long-term research development project  RVO 67985939  (The  Czech Academy of Sciences) , and projects no. 14-36079G  ( Centre of Excellence PLADIAS ) and  P504/11/1028  from the  Czech Science Foundation. PP acknowledges support from  Praemium Academiae  award from The  Czech Academy of Sciences. JMJ acknowledges support from the  ERA-Net BiodivERsA  (project  FFII ), with the national funder  German Research Foundation DFG  ( JE 288/7-1 ), and through the  DFG grant   JE 288/9-1. MV has been funded through the  Severo Ochoa Program for Centres of Excellence in R+D+I  ( SEV-2012-0262 ) and IMPLANTIN ( CGL2015-65346-R ).
Predefined sample groups (selected times of incubation as well as pyrene treatment versus controls) were statistically evaluated based on microbial genera relative abundance data and related to the degradation kinetics. In addition, we enriched microbial pyrene degraders to determine whether pyrene degraders in the soil-compost mixture originate from the soil or from the compost and performed some metagenomic analyses of selected enrichment cultures which were highly difficult to purify.
Analysis of pyrene degrading enrichment cultures The bacteria were analyzed by targeting the 16S rRNA gene. Template DNA of the cultures was amplified in a polymerase chain reaction (PCR) using 1 µl of template DNA. Detailed information on the PCR conditions is given in the Additional file 1. The PCR products were purified using the QIAquick PCR purification Kit (Qiagen, Venlo, Netherlands) and quality checked by 1. 2% formaldehyde agarose gel electrophoresis. 10 to 20 ng of DNA µl -1 were send to commercial Sanger sequencing by GATC Biotech (Konstanz, Germany). The obtained sequences were compared to the database of the Ribosomal Database Project (RDP)  (Cole et al. 2013)  using the SeqMatch analysis tool to identify the closest relative sequences (uncultured bacteria and isolates, only good quality sequences >1200 bases).
2016)  if needed. The quality and completeness of the resulting genomes/bins was evaluated using lineage-specific marker genes and the CheckM software  (Parks et al. 2015 ) (Table  3 ). Phylogenetic anchoring of the reconstructed genomes was carried out based on the full-length 16S rRNA gene sequences using RDP's Naïve Bayes classifier  (Wang et al. 2007 ) based on 16S rRNA training set 14. Further details on Illumina paired-end sequencing and Nextera Mate Pair library preparation and sequencing can be found in the Additional file 1.
Statistics Statistics were performed using the vegan package  (Oksanen et al. 2013 ) and, for indicator genera analysis, the indicspecies package (Cáceres and Legendre 2009) of the R Project for Statistical Computing (R Core Team 2013). For all calculations, a significance level of 0. 05 was selected. For evaluating the quality of sampling, rarefaction curves of samples were compiled and single sample diversity was characterized by calculating Chao1 richness and Shannon's H indices. For the classification of samples, cluster analyses were performed on a Bray-Curtis dissimilarity distance matrix of standardized genera abundance data, derived from 16S rDNA amplicon libraries generated from extracted RNA (cDNA) or DNA, based on group average by the Unweighted Pair Groups Method using Arithmetic means agglomeration algorithm (UPGMA).
Sequence accession numbers The amplicon sequences (Additional file 2: Tables  S4, S5 ) were deposited at the European Nucleotide Archive (ENA) where they are available under the following accession numbers: LT618870-LT619481. Genomic sequences for the enriched bacteria Mycobacterium sp. UC10, Stenotrophomonas sp. UC10, Sphingopyxis sp. UC10 and Microbacterium sp. UC1 are available from the ENA under accession numbers FLQS01000001-FLQS01000092, FLTS01000001-FLTS01000002, LT598653-LT598653 and FLQR01000001-FLQR01000013 respectively. The corresponding sample identifiers and assembly names (in brackets) are: ERS1093932 (MHPYR_PRJEB13035_ v1), ERS1094701 (STPYR_PRJEB13038v1), ERS1093934 (SPPYR_PRJEB13037v1), and ERS1093933 (MIPYR_ PRJEB13036_v1). The draft genomes were also integrated into the MicroScope annotation platform  (Vallenet et al.
Effect of pyrene treatment on the microbial community For elucidating microorganisms responsible for pyrene degradation in soil amended with organics, the microbial communities of soil-compost mixture and fertilized soil based on extracted DNA and partly RNA samples were analyzed by comparing samples with and without (control) pyrene treatment. Since the bacterial diversity in pyrene treatments and controls in both soil-compost mixture and fertilized soil was comparably high (Additional file 1: Table  S3 ), we evaluated in a second step whether structures in the bacterial relative abundance data can be statistically confirmed and visualized. For this investigation we switched from the class level to the genus level. Therefore, the phylotypes from the 16S rDNA amplicon libraries were sorted on genus level and a similarity comparison of the soil-compost mixture and fertilized soil samples based on genera relative abundance   There is no further statistically significant clustering concerning separation of pyrene treatments from controls, both in general and for the respective incubation time.
A frequently used concept in ecology is the analysis of indicator species (or other taxonomic levels) that are associated to a specific habitat or disturbance state and, therefore, "better ecological indicators of environmental change than a habitat generalist" (Cáceres and Legendre 2009). The calculation is based on the group-equalized point-biserial correlation coefficient. This index is a computed Pearson correlation that is suitable for abundance data (in contrast to presence-absence data) based on a large database, without the assumption that the total number of sampled genera is constant for all sites but gives equal weight to all site groups (it is group-equalized) (Cáceres and Legendre 2009). Therefore, the relative abundance data of the 213 genus level phylotypes from soil-compost or 195 genus level phylotypes from fertilized soil based on the 16S rDNA amplicon libraries were separated into the control group (without pyrene addition) and the group with pyrene addition with separated investigation of the incubation time.
Acknowledgements This study was funded by the European Union project MAGICPAH under FP7-KBBE (  245226 ). The authors like to thank Vanessa Lünsmann, Uwe Kappelmeyer, Kerstin Ethner and Ines Mäusezahl (UFZ, Leipzig) for their assistance with molecular biological analyses and Florian-Alexander Herbst for his help with the protein-SIP attempts. In addition, we thank Ramiro Vilchez-Vargas, Cinta Gomez-Silvan, Iris Plumeier, and in particular Ruy Jauregui and Dietmar H. Pieper (HZI, Braunschweig) for their support with 16S rDNA amplicon sequencing, as well as Karine Labadie, Eric Mahieu and Julie Guy (Genoscope) for their help in sample preparation and Illumina shotgun sequencing. The authors are also grateful to Genoscope's LABGeM team for their efforts with the MicroScope genome annotation platform.
As is evident from this brief review, self-images are relevant to a wide range of disciplines and have been utilized in a variety of paradigms. The purpose of this paper is to present a database of self-image norms. At present, to the authors' knowledge, there is no publicly available resource for collating or depositing self-image data. That also means, that aside of attempts to classify the statements generated [e. g. , into consensual and non-consensual  (Kuhn and McPartland, 1954) ; concrete or abstract  (Rathbone et al. , 2008) ; psychological, social, and physical  (Charlesworth et al. , 2015) ], there has been little consideration of the content of people's identity statements. Whilst coding schemes have been developed in the past to classify responses  (Cousins, 1989; Rhee et al. , 1995) , these have been limited to a relatively small number of categories (e.
g. , a maximum of 45 categories and subcategories;  Rhee et al. , 1995)  and thus do not provide information on the frequency of specific self-images (e. g. , being logical). Furthermore, previous studies have not reported the gender ratio or mean age of categories of self-images. Having a database of normative responses, coded by age and gender, will mean we will be better able to consider the content in people's identity statements. The norms presented here represent the coding of 2,412 self-images. However, the main aim here is for this number to increase as other researchers add their data to the database. These norms can be used in a variety of ways. For example, if self-images have been collected from a case study (as is common in memory impairment) or a specific group of participants and the aim is to examine the way these participants have defined themselves, then the norms in the database can provide an age and gender indexed comparison.
METHODS AND MATERIALS Participants The data come from five studies, all of which required participants to generate self-images. Studies 1 and 2 are reported in  Rathbone et al. (2008)  , Mean age 20. 56, SD = 3. 65). These data were collected between 2004 and 2008, using an online questionnaire (Study 2) and paper questionnaires/tasks completed in participants' own homes (Study 1) or in the lab (Studies 3, 4, and 5). The data from studies employing paper questionnaires was collected in the UK whilst the online questionnaire data had potential for worldwide geographical coverage (details of participant nationality were not collected but the online questionnaire was advertised in both the UK and USA).
Coding All 2,412 self-images were coded by CJR and CJAM. This generated a set of 271 categories (including "Unclassified" which referred to any category indexing only one self-image). All 270 categories which indexed two or more self-images were then listed as norms (see Supplementary Material), accompanied by the number of self-images in the category, the full-text of each self-image generated, and the mean age and gender ratio of the participants that generated self-images in that category. For example, "Planner" had a count of three (two cases of "a planner" and one case of "able to plan ahead"), a mean age 28. 67, and was only generated by female participants. Due to the way in which data was processed it is possible in a few cases that there is a discrepancy between the age given in the self-image and the participant's noted age.
Database Websites The fixed data set is publicly available in the UK Data Service ReShare Repository under Collection A of "Memory and the self in ageing"  (Rathbone, 2017)  at: https://dx. doi. org/10. 5255/UKDA-SN-852127. The data set includes a data description document which explains the variables in the excel spreadsheet. There is also a publicly accessible database website hosted by Oxford Brookes University (http://psych. brookes. ac. uk/selfimage) which provides an introduction to the database, a description of its potential uses, a search function, and the option to request a downloadable excel version of the database. One of the primary aims of the website is to form a long-term, growing resource for researchers who wish to deposit their selfimage data. Thus, whilst the original data set, as reported in this paper, is fixed, there is also the option for this database to grow via the self-image website.
Ethics Statement All studies through which data was collected were approved by the University of Leeds School of Psychology Ethics Committee and were conducted according to the principles expressed in the Declaration of Helsinki. Any identifying information (such as participants' own names or the names of their friends, and of places) was removed during the preparation of the database. Written informed consent was obtained from participants in studies 3, 4, and 5. One participant in Study 3 was aged 17, but was deemed able to give informed consent without the need for additional consent from a next of kin, caretaker, or guardian. This was because the participant was enrolled as an undergraduate on a university degree course and because the British Psychological Society Code of Human Research Ethics classes participants aged 17 and over as a non-vulnerable group in terms of age.