dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The authors want to thank the GLOBAL  NETWORK  project for generating part of the database. The GLOBAL  NETWORK  project (https://globalresearchalliance. org/research/livestock/collaborative-activities/globalresearch-project/;accessed 20 June, 2020) was a multi-national initiative funded by the  Joint Programming Initiative on Agriculture ,  Food Security  and  Climate Change  and was coordinated by the  Feed and Nutrition Network  (https://globalresearchalliance. org/research/livestock/networks/feed-nutrition-network/; accessed 20 June, 2020) within the Livestock Research Group of the Global Research Alliance on Agricultural GHG (https://globalresearchalliance. org; accessed 20 June, 2020). The authors want to thank MitiGate, which was part of the  AnimalChange  project funded by the  EU  under Grant Agreement  FP7-266018  for sharing their database with us (http://mitigate.
ibers. aber. ac. uk/, accessed July 1, 2017). Part of  C. Arndt 's,  A. N. Hristov 's, and  S. C. McClelland´s  time in the early stages of this project was funded by the  Kravis Scientific Research Fund (New York, NY)  and a gift from  Sue and Steve Mandel to Environmental Defense Fund. Another part of  C. Arndt 's work on this project was supported by the  FONDECYT  contract ( N° 016-2019-FONDECYT-BM-INC. INV ) and of  A. Hristov 's work by the  USDA (Washington, DC) National Institute of Food and Agriculture Federal Appropriations  under Project  PEN 04539  and Accession Number  1000803. E. Kebreab  was supported by the  Sesnon Endowed Chair Fund  of the  University of California, Davis.
Two major forces have contributed to the fast growth of human genetic data. One from medical research supported by governments and academic institutes; the other from direct-to-consumer (DTC) sequencing companies. While the former benefits from meticulously designed sequencing standards and quality control procedures, the latter comes in various formats and sequencing methods which are subject to changes over time and the particular needs of different companies. Thanks to the general public who shared their DNA data without constraint, here we provide a review for over 7000 genomes made public between 2011 and 2020, and produced by over six DTC sequencing companies. An open source tool-kit to systematically parse, quality check and filter genome files and statistically problematic alleles is provided to prepare consumer DNA datasets for research.
These people represent a huge potential group of participants for genetic research that is seeking to validate findings by a random or untargeted cohort, or trying to study the genetics for a particular phenotype under limited budget. One of the main hurdles in utilizing consumer DNA data for research is that these data vary greatly in sequencing methods and, most importantly, the data quality. For the majority of consumers who have had their DNA genotyped, the versions of genotyping microarray differ for different companies at different time periods. The sequencing methods are often selected and designed based on the individual marketing needs of the companies and the DNA data are processed by a different bioinformatics team in each company. Researchers will not have access to the internal quality control data on the samples/arrays from the various companies; they most often have to deal with whatever raw download format the DNA data are provided to the customer in, which may have undergone varying amounts or types of quality control.
These limitations mean that mass genetic data are most useful to the DTC company responsible for the genotyping, or partners who have commissioned a large project. Here, we will firstly provide a review of the commercial providers, their offering of sequencing methods and genotyping arrays. Secondly, over 7000 genomes were downloaded from open genetic data sharing platforms, the majority coming from the OpenSNP project  [5]. The genomes were parsed, processed and analysed for comparison and to create a pipeline for specific quality control of consumer genomes, thus enabling general genetics research on these data. We have made open source code, a web interface available for user to upload and run the pipeline, and a data-freeze of over 5000 OpenSNP genomes post-process.
To design the current Global Screening Array chip, Illumina started a consortium and collaborated with research institutions as well as DTC companies. Affymetrix (Thermo Fisher) has mostly worked with population genomics studies from research institutions. For instance, its custom UK-Biobank chip that genotyped 500,000 people  [10]. The most appealing advantage of genotyping arrays over next generation sequencing (NGS) is the cost. Therefore, when designing a SNP array chip, it is always necessary to consider targets: whether to cover as many common SNPs as possible for an international audience, or to target a specific population with more population-specific SNPs included, or to target a particular disease or phenotype by including relevant rare SNPs. Also because of the need for cost-effectiveness, the chip size is essentially capped at 1 million, with most products having 600 K to 800 K SNPs, which would represent less than 1% of the over 100 million validated human polymorphisms in dbSNP database  [11].
Analysis and comparison of genotyping arrays from consumer data The above review of genotyping arrays (in Table  1 ) that are broadly provided by the sequencing companies relies on publications and media reports. As there is no universal access to the companies' internal array data and pipelines, this is only of limited use in understanding the nature of the information contained in the raw DNA files they provide for download to their customers. Thus, we provide here an analysis of the arrays from a large sample of consumer data covering multiple companies. Thanks to the thousands of participants of the open genome projects who have donated their genetic data to the public good, the majority from the OpenSNP platform  [24] , we were able to identify various types of arrays used in consumer genotyping over the past decade by at least 6 different companies.
Public genomic data In 2005, George Church talked about recruiting cohort participants who are consented based on expectation of full public data release  [25] , and initiated the Harvard Personal Genome Project (PGP)  [26]. Independently in 2011, Bastian Greshake et al. set up the OpenSNP platform as a crowd-sourcing and crow-funded platform, for consumers of DTC companies to publicly share data and communicate with each other  [5]. For this work we used 4401 genomes from OpenSNP for SNP array comparison (Section 3. 2) and 7076 from OpenSNP and PGP for quality control analysis (Section 4). A total of 5790 genomes were initially downloaded from the OpenSNP webserver in Oct 2020  [5]. We observed from the dates that a continuous supply of data must have accumulated over a period of 9 years (Fig.
SNP array comparison SNP arrays were analysed from 4401 OpenSNP open genomes downloaded at the time of analysis. This includes genetic data from 23andMe (~78%), AncestryDNA (~14%), Family tree DNA, My Heritage (8%) and small numbers from deCODEme, Genes-for-good (non-commercial), IYG, etc. Using OpenSNP data (method in Section 4. 1), 5 major clusters of SNP arrays were identified (Fig. 2 , Table  2 ). The files investigated contain from 500 thousand to 1 million SNPs. The difference between two files is represented by DN, namely the number of SNPs not in common between the two files. It is observed that DN can be as large as 1. 4 million, which means two data of size 500 thousand and 1 million only have 100 thousand SNPs in common with each other. Using DN as the distance measure of SNP array difference between genotyping data, 5 major clusters of SNP arrays can be found after clustering (Fig.
2 ). The genotyping data in each cluster are more than 90% similarity with each other in the SNP array, although some files still contain positions that are not seen in 99% of other files (Fig. 2B ). The companies and possible chip array version for each cluster are deduced from both the clusters, and using header information in the data (Table  2 ). 23andMe data predominantly come from 3 types of arrays, which are deduced to be the Illumina HTS iSelect HD (c1), OmniExpress plus (c4) and GSA (v5). The c4 is the largest genotyping array of ~1 million positions, covering roughly 90% of the c1 and c3. The c3 is made of ancestry-targeted companies, including the Ancestry V1, FamilyTree DNA and My heritage. It is a reduced Illumina OmniExpress array that contains most of the SNPs useful for ancestry-detection.
Genome processing and quality control pipeline To demonstrate and facilitate the use of consumer data, we developed a quality control pipeline to systematically process them (Fig. 3 ). We make this pipeline available as 'GenomePrep' via a web service (http://supfam. mrc-lmb. cam. ac. uk/GenomePrep/) and as an open source package on GitHub. Briefly, the pipeline includes parsing files of various formats from different companies, sanity checking the files and excluding ones that may have problematic data, then sanity checking the SNPs to exclude those with a statistical profile that is not credible. This section describes the details of the quality control procedure and results of processing over 7000 open genetic data files using the pipeline.
Input file format Unlike research projects where data collection is organized and obtained from sequencing at source, consumer data collection is almost impossible to do consistently or reliably. There are Microsoft Word documents, files that have been edited, or files that are somehow rendered unusable by the consumer in the process of handling it. In this procedure, PDF and word documents are excluded whereas text files are sent to the parser. Compressed files such as zipped, gzip, or bzip2 files are decompressed and included if found to contain one single genome flat file, but still excluded if the program can't identify the genome file. For the genotyping files, the parser recognizes data files from companies that are included in the open genome dataset, e. g. 23andMe, AncestryDNA, Family Tree DNA, My Heritage, deCODEme etc.
Identify the assembly version and compare to the reference genome In the first step, the assembly is determined from the genetic file through header lines and a few known signature SNPs, defaulting to GRCh37 if the above information is not found. Then using this version of the assembly, the genetic files are compared to the respective reference genome to check whether the data presents a reasonable human genome with a plausible number of variants. Option is provided to convert genetic data to different versions of genome assemblies through CrossMap  [29].
Check sequencing method for VCF format files The variant-call-format (VCF) format was designed for NGS data, as it presents whole genome data with good storage efficiency by only listing SNPs for which genotypes are non-identical to the reference genome. In the consumer dataset however, we observed that many VCF format files contain over 50% of their entries reported as identical to the reference genome. The reason for this turned out to be that they are the result of genotype array data being converted to an NGS VCF format. Another feature of these genotype-converted VCF files is that many entries lack information about the alternative allele. This is because it is not provided in the genotype data it was converted from. The program will automatically deduce and separate out genuine NGS files, and label those from the genotype-derived VCF files as genotyping results.
Remove SNP positions in the array that are not statistically plausible This is one of the most important and challenging parts of quality control for genotyping data. We have generated lists of SNPs in the public DTC datasets that do not seem credible when compared to an independent source of allele frequencies. Common practice for medical cohorts is to perform SNP quality control using PLINK to exclude the problematic positions before using the data for research  [30] , using statistics such as Hardy-Weinberg equilibrium P-value. In the absence of consistently generated cohort data, we make use of the cluster assigned in step 4. 3. 2 for quality control of the SNPs. Our approach is to use allele distributions from the 2504 genomes in the 1000 genomes project (1000G) as a reference, and for each cluster generate a list of SNPs that fail a Z-score based comparison test.
Results of quality control on test genomes After the pipeline, 6477 DNA data were identified as genotyping results and a typical microarray type is identified for 99% of them (Fig. 3 ). A single combined data-freeze for the 5393 OpenSNP genotyping data whose microarray type was identified by pipeline is available for download from https://supfam. mrc-lmb. cam. ac. uk/ GenomePrep/. It is worth noting that the new chip Array cluster v5 (Illumina GSA) is found to be on average the most similar to the reference (Fig. 4B ), and it also contains the most fraction of questionable SNPs (Fig. 4C ). We also observed that the GRCh36 genomes tend to fall in clusters c3 and c4 (Fig. 4D ), which correspond to v1 of AncestryDNA and the v3 of 23andMe 1 million array respectively. This observation corroborates our previous deduction of array chips shown in Table  2 , as the c1 (23andme v4), c5 (AncestryDNA v2) and v5 (Illumina GSA) all appeared later in the time line.
Most genetic analysis requires a cohort of data to establish variant statistics. So what one consumer can do with the raw genome data is very limited. The power of consumer genomes comes when there is a large amount of data, which is currently accessible as a homogeneous dataset by only the companies that initially conducted the sequencing. Increasingly however, as these companies move to monetise their access to this resource, savvy consumers who control their own data will look outside the original provider for ways to share and use their genome. We will see power move away from providers as large scale online match-making services between consumers and e. g. drug companies emerge. We were only able to develop this quality control pipeline, thanks to the thousands of people who donated their data to open research  [31, 32].
At minimum, hundreds of sequencing results are needed to establish the statistical power necessary to determine alleles that are likely errors in each cluster of genotypes. Genomics and human genetics are scientifically fundamental and commercially valuable  [33]. It is our hope and expectation that, whatever happens in the commercial marketplace, there will continue to be an abundance of volunteers stepping forward to support academic medical (and other) research by contributing their genome. At present it seems likely that individuals will ultimately have major control over how, and by whom their own genome is used. Consumer genetic data is going to keep growing. At the moment consumer data used for research is via consumers opting into research consent, e. g. 23andMe, AncestryDNA; sometimes this data, or access to customers is sold as a package to commercial and academic parties.
Academically, genomic data is generated by institutions who usually recruit participants with particular phenotypes and where sequencing is supported by research grants. At some point, as most people have control of some form of per- sonal sequence data, these two main sources of genome sequencing will cease to dominate. Already some academic studies allow participants to download the DNA file, e. g. the genes-for-good project  [18]. The new generation of DTC sequencing companies are offering more incentives to customers, such as profit-sharing the monetisation of data with the customer, while others such as LunaDNA, offer company shares in exchange for genetic and phenotypic data  [34]. In short, the number of people with their genome sequenced who are willing to participate in research will grow, and they are likely to come from diverse sources and from various methods.
The typical precision is ∼ 100 µas, which is much worse than the theoretical limit of 16. 5 µas determined by  Lacour et al. (2014). This can be attributed to the low and high frequency phase errors present in our data and introduced by instrumental aberrations  (Gravity Collaboration et al. 2021). Furthermore, due to the asymmetry of the uv-plane, we used the correlation coefficient ρ to properly describe the confidence intervals and the correlations between the right ascension and the declination offset. Finally, a spectrum of the companion for each epoch of data was obtained from the amplitude of the ratio of the coherent fluxes. The host star is essentially unresolved in our observations such that we did not need to correct this ratio for the visibility of 2 Python package available on GitHub upon request.
Astrometric analysis From the interferometric observations with GRAVITY we obtain two new astrometric data points with an unprecedented precision of ∼ 100 µas for HD 206893 B (cf. Table  2 ). Together with astrometric data from the literature (cf. Table  A. 1), we estimate the orbital parameters of HD 206893 B with orbitize! 3   (Blunt et al. 2020) , which infers the posterior distribution of the orbital parameters through Markov Chain Monte Carlo (MCMC) sampling with ptemcee 4   (Foreman-Mackey et al. 2013; Vousden et al. 2016). We initialize the sampler with 20 temperatures, 500 walkers, and 50000 steps per walker. By visual inspection  Notes. Scenario 1 is constrained by the data only and scenario 2 has an additional constraint on coplanarity with the debris disk. of the walker chains, we assess convergence and reject the first 40000 steps before computing the posterior distribution from each walker at the lowest temperature.
Gaia proper motion anomaly An independent constraint on the mass of HD 206893 B can be obtained from the proper motion anomaly of its host star (HD 206893 A) measured by Gaia  (Kervella et al. 2020). While the proper motion measured by Gaia (Gaia Collaboration 2018) traces the photocenter of the system, the long-term proper motion derived from the difference between the Gaia and Hipparcos (van Leeuwen 2007) positions traces the barycenter of the system, once corrected for the influence of the companion at the time of the Hipparcos and Gaia measurements. The difference between the proper motion measured by Gaia and the long-term proper motion is the proper motion anomaly that must be caused by one or multiple faint companions. Grandjean et al. (2019)  have already found that the proper motion anomaly measured between the Gaia DR2 and the Hipparcos data cannot be explained by HD 206893 B alone and suggested the presence of another closer-in companion of ∼ 15 M Jup (HD 206893 C).
50   (Buchner et al. 2014; Feroz et al. 2009 Feroz et al. , 2019)). Several hypotheses have been put forward to explain the extreme redness of HD 206893 B, most notably extinction by local dust, either extra-photospheric or in the form of a circumplanetary disk by  Milli et al. (2017) ,  Delorme et al. (2017), and Ward-Duong et al. (2021). Other possibilities like reddening by interstellar dust or extinction by the debris disk could be mostly ruled out. Ward-Duong et al. (2021)  could not find any significant interstellar extinction toward the host star based on its photometry, and we can confirm this finding by visual inspection of stellar 6 https://github. com/JohannesBuchner/PyMultiNest Ca-lines in high-resolution spectra of HD 206893 A (A. -M. Lagrange, private communication). It seems unlikely that there is an interstellar dust cloud that is obscuring HD 206893 B but not its host star, which is separated by only ∼ 250 mas.
30-1. 34, the DP grid prefers large grains with a geometric mean size of ∼ 2. 29 µm and a geometric standard deviation of ∼ 1. 17. This is a difference in geometric mean grain size of almost an order of magnitude. We note that the DP grid uses a different cloud model than the BT and ER grids. With DP, gas is mixed to high altitudes where dust then forms and grows as it rains down. With BT and ER, 7 https://github. com/bsumlin/PyMieScatt the cloud model from  Ackerman & Marley (2001)  is used which assumes that the cloud particles are mixed from the cloud base upward. These fundamentally different approaches could cause the observed difference in predicted dust grain size. Helling et al. (2008a)  have further found that the dust to gas ratio predicted by the DRIFT model is larger than the one predicted by the Settl model at small pressures, where the mean grain size is below 1 µm.
Astrometry In Section 3. 1, we infer the orbital parameters of HD 206893 B for two different scenarios. Scenario 1 is constrained by the data only and scenario 2 has an additional constraint on coplanarity with the debris disk of the system observed by  Milli et al. (2017)  and  Marino et al. (2020). By comparison with earlier works from  Grandjean et al. (2019)  and  Ward-Duong et al. (2021) , we find that the GRAVITY data resolves the degeneracy between a lower eccentricity, larger semimajor axis and a higher eccentricity, smaller semimajor axis orbit by preferring the latter of these in scenario 1. This is interesting because this orbital solution for HD 206893 B is only roughly aligned with respect to the debris disk of the system (i m < 34. 4 deg). Marino et al. (2020)  mention that a misalignment between the orbit of HD 206893 B and the debris disk of the system should be unlikely given its age of at least 50 Myr  (Delorme et al.
2017). They argue that HD 206893 B should align with the debris disk of the system due to secular interactions on timescales of only ∼ 10 Myr. In scenario 2, where we enforce alignment between the orbit of HD 206893 B and the debris disk of the system, we clearly find that the lower eccentricity, larger semimajor axis orbit is preferred. This is in agreement with  Marino et al. (2020) , who obtained the same result without the additional GRAVITY data, and suggests that the GRAVITY data alone prefers a slight misalignment between the orbit of HD 206893 B and the debris disk of the system. If this misalignment is confirmed by future GRAVITY observations, an explanation for it needs to be found. One possibility would be a significantly younger age (< 50 Myr) for the system. We note that such a young age would be in agreement with the age constraint set by comparing our best fit dusty BT and ER models and the spectral retrievals with evolutionary tracks (cf.
Grid retrievals In Section 4, we analyze the near-infrared spectrum of HD 206893 B. We obtain a dense spectral coverage between 1-2. 5 µm by combining data from SPHERE, GPI, and GRAVITY that we extend up to ∼ 5 µm with VLT/NACO photometry from  Stolker et al. (2020). While  Delorme et al. (2017)  and  Ward-Duong et al. (2021)  found that currently available atmospheric models for giant planets and brown dwarfs fail to predict the extremely red color and the very shallow 1. 4 µm water absorption feature of HD 206893 B at the same time, they also compared the spectrum of HD 206893 B to those of other dusty, lowgravity or young M and L-dwarfs and found that none of these objects could reproduce all spectral features of HD 206893 B. Hence, both of these authors tried to reconcile the spectrum of HD 206893 B with those of other dusty, low-gravity or young M and L-dwarfs by including additional extinction by highaltitude dust clouds.
Acknowledgements. The authors would like to thank  Rob De Rosa  for his insights on orbit fitting and the comparison with the GPI results. This research has made use of the  Jean-Marie Mariotti Center Aspro service 11. P. M. and T. H. acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Advanced Grant  Origins  83 24 28. T. S. acknowledges support from the  Netherlands Organisation for Scientific Research (NWO)  through grant  VI. Veni. 202. 230. This work was performed using the AL-ICE compute resources provided by  Leiden University. A. V. , G. O. , and M. H.