dataesr/scientific-paragraphs-categorization huggingface dataset:
Rescuing legacy data and constraints on data use An increase in the availability and ease of use of data repositories raises the prospect of making data available from previously published studies where the data were not released at the time of publication. Digital datasets can be uploaded to online data repositories and linked to past publications. At present, there are no policies or mechanisms we are aware of among journals and publishing houses to link archival publications to newly deposited data. However, there is no material technical barrier to salvaging legacy data in this way. Publishers are likely to welcome such an initiative as it would obviously improve data visibility, facilitate reproducibility, and probably rejuvenate old publications in terms of access, citations and, ultimately, their marketability.
Obtaining digital characterizations of morphology can be time-consuming and expensive, and researchers rarely exhaust their data with the first publication. Funders and publishers are increasingly removing choice over whether to release supporting data, and so it can seem unfair that the researchers who generated datasets have to subsequently compete to exploit them further. This can be particularly difficult for lone early-career researchers potentially competing with large experienced research groups  [33]. One potential solution to this would be the introduction of time-limited embargos, which can already be facilitated by some data repositories. However, such embargos violate the most basic tenet of open data: that of removing barriers to assessing the reproducibility of research  [48].
Invariably, much more is at stake in such projects, and though these informal agreements are rarely violated, they are generally well policed by the peer review process  [52] , and by the reputational damage suffered by those who choose not to observe these agreements. Practice in the genomics community underscores the point that there is more to gain from open data than the warm glow of altruism  [51, 53]. Not only has it led to greater and more rapid scientific advance  [48, 51] , it can lead to material personal gain, through proposals for collaborative exploitation of published data, both to achieve stated research objectives, and to achieve new objectives that would not be possible without unforeseen collaborators  [51, 53]. Citation and access-tracking of published datasets also provide credit to the authors  [31].
Attribution of authorship is mandated under CC-BY licences and is in any case integral to the academic culture. Many journals already mandate citation of published datasets, not (or not merely) the publications describing research based upon them; this must become common practice. Further mechanisms for encouraging researchers to share their data should only add to this motivation, such as explicitly evaluating the open sharing of data in hiring, promotion or other reward processes. Nevertheless, data can be associated with ethical sensitivities that may require the withholding, or restriction on public distribution, of data (e. g. anthropology or medical science  [54, 55] ). In such instances, the issues that apply should be clearly defined so that beyond these boundaries researchers and publishers can follow an ethos of open-data publication.
Outstanding challenges While the principle of open data has been mandated by the majority of funders  [32] , publishers, physical repositories and researchers are all scrambling to meet the resulting challenges. Above all, the competing interests over ownership of digital data need to be resolved between (i) funders who pay for research, (ii) researchers who collect specimens and create rspb. royalsocietypublishing. org Proc. R. Soc. B 284: 20170194 the digital datasets, (iii) research facilities where data are collected, (iv) museums that have a duty of care for the physical specimens and (v) research publishers. Funders, researchers and publishers may have converged on an ethos of open data. However, the institutions that are responsible for the physical specimens have not obviously been invited to engage in the development of open-data policy, and yet it is museums that will have to change most in terms of their policies on the nature of what they consider intrinsic aspects of the physical specimens that they hold in their care.
It is also important that systems are developed to ease the submission of such data, and facilitate secure, anonymized distribution of data to reviewers. Dryad offers an integrated submission system where publishers can coordinate submission of a manuscript with submission of data, which can then be accessed securely by referees and editors. For non-integrated journals, an interim solution may be to host data at a temporary, hidden URL that can be forwarded to the reviewers via the journal. Authors may be cautious about sharing such data ahead of an article being accepted for publication, and there should be a clear policy governing the restrictions of use for reviewers.
Acknowledgements. We thank  Zosia Beckles  (data. bris), Else-Marie Friis  ( NRM, Stockholm ),  Mark Hahnel  (figshare),  Iain Hrynaszkiewicz  ( Springer Nature ),  Elizabeth Hull  (Dryad),  Phil Hurst  ( Royal Society Publishing ),  Rhiannon Meaden  ( Royal Society Publishing ),  Sowmya Swaminathan  ( Springer Nature ),  Stuart Taylor  ( Royal Society Publishing ) and  Sally Thomas  ( Palaeontological Association ) for discussion.
Development of back-projection algorithms can improve signal to noise ratio in generated image stacks and, hence, recent open-data mandates at synchrotron facilities require archiving of the radiograph projections, not the resulting slice data mode imaging method essential (for verification) recommended (as best practice) 3D models tomography -full-resolution image stack (e. g. TIFF) -prepared dataset (i. e. segmented images) -final 3D models used in study (e. g. STL) consisting of image stack and/or project -text file with description of scan settings a , voxel folder (e. g. AVIZO label fields, SPIERS size, techniques used to produce 3D models, masks) and specimen information (e. g. copyright, -unregistered image stack (for physical and repository, and accession number) optical tomography) laser or structured -final 3D models used in study (e.
g. STL) -3D models retaining texture information b light scanning -text file with description of scanner settings, (e. g. PLY or OBJ) resolution, techniques used to produce 3D -original capture data (i. e. data acquired models, and specimen information (e. g. by scanner) copyright, repository, and accession number) -3D models retaining texture information b (e. g. PLY or OBJ) -original capture data (i. e. photographs) additionally for downstream functional analyses: morphometrics -landmark coordinates and rules defining automated landmark capture -images used in 2D landmark analysis (e. g. TIFF) -3D models used in 3D landmark analysis (e. g. STL) -text file with description of how analysis was performed and specimen information (e. g. copyright, repository, and accession number) functional -3D models used in functional analysis -project file with results analyses -project file with details of material properties and boundary conditions used in analysis photogrammetry -final 3D models used in study (e.
g. STL) -text file with description of how images were acquired, scale, techniques used to produce 3D models, and specimen information (e. g. copyright, repository, and accession number) a This should include: details of the scanner, current, voltage, number of projections, exposure time and filter thickness (if any). b Essential if critical to the analysis. rspb. royalsocietypublishing. org Proc. R. Soc. B 284: 20170194 3D model. In such instances, prepared datasets should be released. No standard file format exists, but labels and masks can be released in the native formats by the software used to generate them, or as binary image stacks, which can then be readily reconstructed as a 3D model in a variety of software packages [1,42].
Methods Darmor-bzh and Tapidor genome assemblies All Tapidor reads were cleaned using sickle  (Joshi and Fass, 2011)  and normalized using khmer v1. 0 normalize_by_median. py  (Brown et al. , 2012; Crusoe et al. , 2015). VelvetOptimiser   (Gladman and Seemann, 2012)  was used with Velvet (Zerbino and Birney, 2008) to assemble contigs. Contigs greater than 1 Kbp were sorted by comparison with the Darmor-bzh assembly using blastn  (Altschul et al. , 1990)  with an e-value of 1e-6. Contigs with two highest-scoring alignments within 1% were unplaced. Contigs were then sorted using LASTZ  (Harris, 2007)  and a custom script (LASTZSorter. py, available at http://appliedb ioinformatics. com. au/index. php/Darmor_Tapidor). MSTMap  (Wu et al. , 2008)  was used to calculate the genetic map using the unimputed SNPs of the DH population (distance_function: kosambi, cut_off_p_value: 0.
0000001, no_map_size 2, miss-ing_threshold 0. 1). SGSautoSNP  (Lorenc et al. , 2012)  was used with the SkimGBS pipeline  (Bayer et al. , 2015)  to call SNPs and genotypes using either the Tapidor or Darmor-bzh genome as reference  (Chalhoub et al. , 2014). ContigPlacer was used to place unplaced contigs in the Darmor-bzh and Tapidor assemblies and is available at https:// github. com/philippbayer/contigPlacer.
rapa, B. oleracea and B. napus unigenes from UniGene (NCBI Resource Coordinators 2013) were used as evidence. Predicted genes with an AED-score of 1, shorter than 100 bp, or carrying transposase-related domains were removed from subsequent analysis. Both assemblies were assessed using CEGMA v2. 5  (Parra et al. , 2007) and BUSCO v1. 1. b1 (Simão et al. , 2015)  with the early release plants dataset. Predicted transcripts and proteins were renamed according to the standardized Brassica nomenclature  (Østergaard et al. , 2008).
Gene-level comparison between Darmor-bzh and Tapidor MCScanX  (Wang et al. , 2012)  was used to analyse collinearity between the Darmor-bzh and Tapidor genes and to assign predicted genes to blocks of at least five collinear genes. Custom scripts were used to parse the MCScanX output. Genes were compared with the Tapidor pseudomolecules using blastp with an e-value cut-off of 1e-10. The scripts are available at http:// appliedbioinformatics. com. au/index. php/Darmor_Tapidor under 'Colinearity analysis'. Assessment of gene loss used the SGSGeneLoss pipeline v0. 1  (Golicz et al. , 2014). Public Darmor-bzh (BioProject ID: ERP005275, ERP005532) reads were aligned using bowtie2 (settings: -end-toend, -sensitive)  (Langmead and Salzberg, 2012)  and extracted using samtools  (Li et al. , 2009).
Gene expression levels of the genes predicted to be absent were measured using bowtie v1. 1. 2 (Langmead et al. , 2009)  and eXpress v1. 5. 1  (Roberts and Pachter, 2012)  and the same RNA-Seq data as used for the annotation. Predicted genes showing less than five unique fragments mapping in a library were assumed to be not expressed in that library to account for possible mismappings from expressed homeologous genes. Genes were functionally annotated using blastp and UniProtKB Swiss-Prot. GO terms were determined from Swiss-Prot results and topGO as used to predict enriched  GO-terms (Alexa and Rahnenfuhrer, 2010). To identify collapsed and repeated regions in the Tapidor and Darmor-bzh assemblies, reads from each cultivar were mapped to their respective genome reference assemblies using SOAPaligner v2.
Acknowledgements The authors would like to acknowledge funding support from the  Australian Research Council  (Projects  LP110100200 ,  FT130 100604 ,  LP130100925 ,  LP140100537  and  DP160104497 ), from the  UK Biotechnology and Biological Sciences Research Council (BBSRC)  to GK ( BB/E017797/1 ) and IB ( BB/E017363/1 ) and from the  National Basic Research and Development Programme of China  ( 2006CB101600 ). This work was supported by resources provided by the  Pawsey Supercomputing Centre  with funding from the  Australian Government and the Government of Western Australia , the  Australian Genome Research Facility (AGRF) , the  Queensland Cyber Infrastructure Foundation (QCIF)  and the  Australian Partnership for Advanced Computing (APAC).
Data access All raw sequence reads for the cultivar Tapidor used for assembly have been submitted to NCBI BioProject (https://www. ncbi. nlm. nih. gov/bioproject/342383). The Tapidor 9 Ningyou-7 DH data are available at http://www. ncbi. nlm. nih. gov/bioproject/PRJNA 274890 The assemblies and annotations along with scripts and other information described in this manuscript are available at http:// appliedbioinformatics. com. au/index. php/Darmor_Tapidor.
(Triplets that share two points but form a "Y" shape are not adjacent. ) Each vertex in the graph is a triplet and adjacent triplets s and t share an edge with value e s + e t , the sum of their errors (lines 13 to 17). Note that the shortest paths found by Floyd-Warshall (line 18) will be shortest in terms of the sum of the errors. This does not correspond to the NFA formula but is only a heuristic step, without any probabilistic interpretation, justified in what follows. All the candidate chains provided by Floyd-Warshall are For large datasets, the number of valid triplets can be restricted even further by setting a lower error threshold. For example, using E max ( √ N ) (the maximum error so that a chain of length √ N would no longer be meaningful, Equation (  5 )), instead of E max (N ).
We added a conditional statement to the algorithm so that E max ( √ N ) is used as the threshold if N > 200. Otherwise the threshold is set to E max (N ). In practice this implies that if the input dataset contains a single very long curve, it will be detected as multiple shorter curves. The second stage of the algorithm is to compute the NFA of each path found by the Floyd-Warshall algorithm (Algorithm 1, lines 19 to 21). To do this, the path between each of the T (T -1)/2 pairs of triplets needs to be evaluated. In the extreme case that every path is of length T (meaning every path goes through every triplet), the complexity of this stage would also be O(T 3 ). In practice this is always much lower. The final stage of the algorithm is the computation of the masking principle (Algorithm 2).
Again, let us consider a theoretically extreme case. Suppose all the T (T -1)/2 paths found by Floyd-Warshall are meaningful. Because of the masking principle, each triplet can participate in only one meaningful, non-redundant chain. Thus, the final list of meaningful chains can be at most of size T. In this extreme case, comparing the paths in the two lists would have a complexity of O(T 3 ). In practice this would rarely be the case. All in all, the total computational complexity of the algorithm is O(T 3 ) = O(N 3 ). In practice, the total computation time will depend on the amount of structure present in the dataset. In a set of random points, no meaningful chains will be found and the second and third stages of the algorithm will require no computations. To evaluate the dependence of the computation time on the amount of structure present in the dataset, we carried out the following experiment.
We created six different datasets, all of which have 500 points. The first dataset contains only random points. The second dataset contains a perfectly spaced circle of 100 points, and the other 400 points are randomly distributed. In the third dataset, the circle has 200 points, and the background has 300 random points. This pattern is continued until in the last dataset the circle has 500 points and there are no background points. The datasets and the running times, as well as the algorithm result are shown in Figure  3. The computation times were obtained with a 2. 27Ghz Intel Xeon X7560 CPU with 32GB of RAM. The algorithm is implemented in Matlab and no parallelization is used.
Figure3: Evaluation of the computation time of the algorithm related to the amount of structure in the dataset. From left to right, the circle contains 0, 100, 200, 300, 400 and 500 equally spaced points, with respectively 500, 400, 300, 200, 100 and 0 random points in the background. The computational complexity of the algorithm is O(T 3 ), where T is the number of triplets. The more qualifying triplets in the dataset, the longer the algorithm will take.
Miscellaneous Template DNA fragments were hybridized to the surface of paired-end (PE) flow cells (HiSeq 2500 sequencing instruments) and amplified to form clusters using the Illumina cBotTM. Paired-end libraries were sequenced using 2 Â 120 cycles of incorporation and imaging with Illumina SBS kits. For the HiSeq 2500, 2 Â 101 cycles with SBS kits v3 were employed. Each library was initially run, assessing optimal cluster densities, insert size, duplication rates and comparison to chip genotyping data. Following validation, the desired sequencing depth (>60X) was then obtained. Real-time analysis involved conversion of image data to base-calling in real-time.
GWAS discovery overview. We meta-analysed study-specific association results from 57 Hapmap-imputed GWAS and 22 studies with Metabochip, including up to 241,258 (87% European descent) individuals (51,080 current smokers and 190,178 nonsmokers) while accounting for current smoking (SMK) (Methods section, Supplementary Fig. 1 , Supplementary Tables  1 2 3 4 ). For primary analyses, we conducted meta-analyses across ancestries and sexes. For secondary analyses, we conducted meta-analyses in European-descent studies alone and sex-specific meta-analyses (Tables  1 2 3 4 , Supplementary Data 1-6). We considered four analytical approaches to evaluate the effects of smoking on genetic associations with adiposity traits (Fig. 1 , Methods section). Approach 1 (SNPadjSMK) examined genetic associations after adjusting for SMK.
8 ). We demonstrate that the two approaches yield valid type 1 error rates and that Approach 1 can be more powerful to find associations given zero or negligible quantitative interactions, whereas Approach 2 is more efficient in finding associations when interaction exists. Modification of genetic predisposition by smoking. Approach 3 directly evaluated GxSMK interaction (SNPint; Table  3 , Supplementary Data 1-6, Fig. 2 , Supplementary Figs  9  and 10 ). For primary meta-analysis of BMI, two loci reached GWS including a previously identified GxSMK interaction locus near CHRNB4 (ref. 3), and a novel locus near INPP4B. Both loci exhibit GWS effects on BMI in smokers and no effects in nonsmokers. For CHRNB4 (cholinergic nicotine receptor B4), the variant minor allele (G) exhibits a decreasing effect on BMI in current smokers (bsmk ¼ À 0.
We identified rs1809420, near CHRNA5-CHRNA3-CHRNB4, for BMI in the men-only, combined-ancestries meta-analysis (Supplementary Data 1). Power calculations demonstrate that Approach 4 has increased power to identify SNPs that show (i) an effect in one stratum (smokers or nonsmokers) and a less pronounced but concordant effect in the other stratum, or (ii) an effect in the larger nonsmoker stratum and no effect in smokers (Fig. 3 ). In contrast, Approach 3 has increased power for SNPs that show (i) an effect in the smaller smoker stratum and no effect in nonsmokers, or (ii) an opposite effect between smokers and nonsmokers (Fig. 3 ). Our findings for both approaches agree with these power predictions, supporting using both analytical approaches to identify GxSMK interactions. Enrichment of genetic effects by smoking status.
Many genes implicated in BMI are involved in appetite regulation and feeding behaviour  1. For waist traits, our results adjusted for BMI likely highlight distinct pathways through which smoking alters genetic susceptibility to body fat distribution. Overall, our results indicate that more loci remain to be discovered as more variance in the trait can be explained as we drop the threshold for significance. Functional or biological role of novel loci. We conducted thorough searches of the literature and publicly available bioinformatics databases to understand the functional role of all genes within 500 kb of our lead SNPs. We systematically explored the potential role of our novel loci in affecting gene expression both with and without accounting for the influence of smoking behaviour (Methods section, Supplementary Note 3, Supplementary Tables  10 11 12 ).
R 2 SMK = 0. 01% R 2 SMK = 0. 07% R 2 NONSMK = 0. 01% R 2 NONSMK = 0. 07% R 2 SMK = 0. 34% R 2 NONSMK = 0. 34% R 2 NONSMK Opposite direction Consistent direction R 2 SMK Opposite direction Consistent direction R 2 NONSMK Opposite direction Consistent direction R 2 SMK Opposite direction Consistent direction R 2 NONSMK Opposite direction Consistent direction R 2 GRIN2A activity  34, 35  , are implicated in treatment for obesityassociated disorders, including binge-eating disorders and morbid obesity (ClinicalTrials. gov identifiers: NCT00330655, NCT02334059, NCT01997515, NCT01724983). Memantine is under clinical investigation for treatment of nicotine dependence (ClinicalTrials. gov identifiers: NCT01535040, NCT00136786 and NCT00136747). While our lead SNP is not within a characterized gene, rs4141488 and variants in high LD (r 2 40.
1 ). Our primary meta-analyses focused on results from all ancestries, sexes combined. Secondary meta-analyses were performed using the European-descent populations only, as well as stratified by sex (men-only and women-only) in all ancestries and in European-descent study populations. Cohort descriptions and sample sizes. The GIANT consortium was formed by an international group of researchers interested in understanding the genetic architecture of anthropometric traits (Supplemental Tables 1-4 for study sample sizes and descriptive statistics). In total, we included up to 79 studies comprising up to 241,258 individuals for BMI (51,080 smokers, 190,178 non-smokers), 208,176 for WCadjBMI (43,226 smokers, 164,950 non-smokers), and 189,180 for WHRadjBMI (40,543 smokers, 148,637 non-smokers) with HapMap II imputed genome-wide chip data (up to 2.
We did not use a variable of 'ever smoker vs. never' as it increases heterogeneity across studies, thus adding noise; also this definition would make harmonization across studies difficult. Genotype identification and imputation. Studies with GWAS array data or Metabochip array data contributed to the results. Each study applied study-specific standard exclusions for sample call rate, gender checks, sample heterogeneity and ethnic group outliers (Supplementary Table  2 ). For each studies (except those that employed directly typed MetaboChip genotypes), genome-wide chip data was imputed to the HapMap II reference data set. Study level analyses. To obtain study-specific summary statistics used in subsequent meta-analyses, the following linear models (or linear mixed effects models for studies with families/related individuals) were run separately for men and women and separately for cases and controls for case-control studies using phenotype residuals from the models described above.
1 ). GC correction for GWAS data used all SNPs, but GC correction for MetaboChip data were restricted to chip QT interval SNPs only as the chip was enriched for associations with obesity-related traits. Any studylevel GWAS file with a lambda 41. 5 was removed from further analyses. While we established this criterion, no study results were removed for this reason. Meta-analyses. Meta-analyses used study-specific summary statistics for the phenotype associations for each of the above models. We used a fixed-effects inverse variance weighted method for the SNP main effect analyses. All metaanalyses were run in METAL  58. As study results came in two separate batches (Stage 1 and Stage 2), meta-analyses from the two stages were further metaanalysed (Stage 1 þ Stage 2). A second GC correction was applied to all SNPs when combining Stage 1 and Stage 2 meta-analyses in the final meta-analysis.