dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015) , and which represent the most common aDNA damage  (Briggs et al. , 2007). All the steps described above are implemented in the CASCADE processing pipeline (Figure  1 ). At each stage of the pipeline, primary and secondary data relating to the experimental step are recorded. Primary data include attributes directly related to a given specimen, e. g. , the condition of the sample, the amount of powder obtained, or the volume of enzyme used for treatment. Secondary data on the other hand refer to attributes that are only indirectly related to the specimen e. g. , the coordinates of the excavation site, the return address of the sample provider, or the details of the protocol used for processing. All these data are handled by CASCADE leveraging a relational database recording 1,155 attributes (fields or columns) grouped into 97 different record types (stored as record sets or tables) which are linked together through a network of 374 private key -foreign key relations (Tables  1 2 3 ).
Upon registration of a given sample, the system generates a unique identifier and then incorporates additional information as the sample progresses through the pipeline. This feature enables unambiguous identification at every step until the sample reaches the final stage of data analysis. The partitioning of data and their interconnection by a web of relations allows users to flexibly combine individual data sets to query exactly the information required for a certain analysis or task. This flexibility is made available to users through a query system allowing them not only to create queries, but also to store and re-run them at a later stage (e. g. , after new content has been added to the database). In effect, it grants users the power to add to the functionality of CASCADE as they see fit.
The Database User Interface The database interface consists of two main parts, corresponding to the "Presets" and the "Experiments" sections. The "Presets" section describes all data that can exist independently of experiments but which form the majority of dependencies for experimental entries. These include, among other things, collaborator and user contact information, excavation sites, taxa, materials, protocols, and oligo-nucleotides used for preparing DNA library adapters, library indexing and amplification. The "Experiments" section mainly handles the actual experimental data, but also includes data types that are closely associated, such as imaging records (if available), sample groups, and analytical results of different types, depending on the experimental step considered. In a typical use case scenario, any newly arrived sample is first checked for its "Presets" requirements (e.
"Paleomix runs" stores the data parsed from the report files produced by the Paleomix pipeline. "Sessions" logs which specimens were processed in the laboratory at the same time. "Sample groups" allows storing the relation between different samples (e. g. , from the same individual) and provides the attachment point for Paleomix runs. Finally, "Imaging," deals with the photographic documentation of samples as well as 3D scans to document the samples' state prior to processing. "Waiting list" stores which specimens are being processed, booked, or waiting for the next step. "Defaults" allows users to set presets for each field displayed on forms that handle experimental data types in order to make data entry faster and more streamlined by reducing repetition. "Queries" store each query generated by CASCADE's query system, while "Administration" includes tables for registered database users, registration permissions, events that modified the database etc.
Waiting entries have an edit button instead, which leads to a menu allowing all users to attach notes to the entry, e. g. , special experimental conditions must be applied, location of the material, etc. communicated through the waiting list and also displayed on the sample's detail page. For each pipeline step a new entry is automatically created ensuring that the list is always up to date and everyone interested about the current status of the sample and the progress it has made. To illustrate this feature, we have pre-filled CASCADE with a toy example, which shows specimens at different stages of the processing pipeline. More details about the database interface can be found in the companion manual.
A specimen's progress through the different experimental steps (i. e. , pulverization, DNA extraction, USER treatment, library construction, qPCR, amplification, capture/target enrichment, pooling, and sequencing) can be easily monitored via a "Status bar" feature available on the detail page of the corresponding sample (Figure  3A ). A click on the icon of any type of experiment in this bar (Figure  3B ) displays a list of all related entries of that type stored in the database (Figure  3C ) and links directly to the corresponding detail pages should the processing of the focal entry be completed. While this feature allows tracking individual samples, tracking whole sets of samples is often equally desirable. For this reason, three higher-level detail menus, one each for collaborators, excavation sites, and taxa, were implemented (Figures  4 5 6 ).
The Query System As mentioned above, one of the great strengths of relational databases is to offer the possibility of freely combining data from different related records. This feature facilitates the detection of patterns or correlations between parts of the data that otherwise might not have been visible. This kind of data manipulation is usually done using Structured Query Language (SQL) queries. However, due to the vast possibilities of SQL queries and the power that they have over the data and structure of a database, making them available to users is very risky. This risk is both due to the damage that inexperienced users can inadvertently cause but also due to intentional damage by hackers. Another problem is that even SQL queries providing simple output can be complex to write, especially for beginners.
This property allows evaluating how far samples have progressed through the pipeline which helps this collaborator to assess on demand whether enough data have already been collected to proceed with a project's next step. FIGURE 7 | Graphical representation of database queries. Colored arrows (red and orange) demonstrate how different paths through the same connected data sets provide answers to various questions (described at dotted circle). All queries in orange are described in the main text and provided with CASCADE. The red queries illustrate other, less generic queries used to answer very specific questions and hence were not pre-built. Gray rectangles with black headers represent the tables dealing with the data described in each header. Solid black lines represent the relations existing between those data sets.
A search query created with our interface first combines the requested data sources (e. g. , tables or other previously created queries) based on given criteria, filters the combined data according to user specifications, then selects whatever data fields were requested by the user and returns the result in table format. which, in contrast to their built-in counterparts, provide the same filtering possibilities available in advanced searches. We have provided three such queries in the "Tables" section of the basic search feature. Another possible use of the query feature is the generation of informative labels for bags, bottles, and tubes used at any step of the experimental pipeline handled by CASCADE. To achieve this result, customized queries retrieve exactly the data required for any individual laboratory's labeling needs and are exported as tabdelimited text files that can serve as input for most label-printing software and equipment available at the host laboratory.
The data obtained can also be used for the production of one-dimensional and two-dimensional barcodes (e. g. , QR codes). Due to their data density, the latter type is especially useful for labeling samples and tubes. This feature allows to store and retrieve all essential information about a specimen by simply scanning the attached barcode with barcode scanner or even a smartphone (in our laboratory we make use of "Barcode Scanner" for Android based on the ZXing open source barcode scanning library), without the need for a connection to CASCADE. Barcodes can also be used to directly access all sample information stored in the database. For this, users generate batches of IDs for their own personal use which can be assigned to any experimental data type and enable the recovery of the related data record.
ACKNOWLEDGMENTS We thank  Dr. Naveed Khan  and the staff of the  AGES research laboratory (Archeology, Genomics, Evolution and Societies) , especially  Dr. Morgane Gibert ,  Dr. Catherine Thèves , and  Dr. Tomasz Suchan , for their input on the database design, help with data restructuring, database testing and quality control. We also thank  Corentin Deppe  for his help in input data management.
It also contributes to the long-term preservation of important experimental information that may prove essential for the integration of available data to future projects, especially as the underlying methodology is constantly evolving. In order to safeguard all experimental information handled by CASCADE, we have implemented two separate backup mechanisms. The first mechanism is configured during the installation and setup of the VM. It automatically generates full backups on a weekly basis in addition to daily incremental ones that allow a full reset of the database to its last functional state. The second mechanism can be manually triggered from inside CASCADE so as to force data download at critical stages. Like for the first mechanism, the output produced this way can be used to re-initialize the database.
AUTHOR CONTRIBUTIONS LO conceived the project and coordinated work. DD and AFa designed the database schema and coordinated the data restructuring process. DD created the virtual servers, figures, and programmed the database. AFa developed the pool feature concept, managed the input of all laboratory members on the database design, and curated the data. AFr developed the tag and sample group feature concepts and performed break testing of the database. AS-O developed the waiting list feature concept. XM tested installation procedures. AFa, SS, LT-C, LC, SW, CD, AFr, and AS-O tested the database. DD, AFa, and LO wrote the manuscript, with input from all coauthors. All authors contributed to the article and approved the submitted version.
Dense sampling of bird diversity increases power of comparative genomics Whole-genome sequencing projects are increasingly populating the tree of life and characterizing biodiversity  [1] [2] [3] [4]. Sparse taxon sampling has previously been proposed to confound phylogenetic inference  5  , and captures only a fraction of the genomic diversity. Here we report a substantial step towards the dense representation of avian phylogenetic and molecular diversity, by analysing 363 genomes from 92. 4% of bird families-including 267 newly sequenced genomes produced for phase II of the Bird 10,000 Genomes (B10K) Project. We use this comparative genome dataset in combination with a pipeline that leverages a reference-free whole-genome alignment to identify orthologous regions in greater numbers than has previously been possible and to recognize genomic novelties in particular bird lineages.
Although the generation of genomes is now more routine, an immediate challenge is how to efficiently compare large numbers of genomes in an evolutionary context. A critical first step is the accurate detection of orthologous sequences. In this study, we release a large-scale dataset of bird genomes, which we use to establish a framework for comparative analysis. We provide insight on how scaling-up genome sampling assists in our understanding of avian genomic diversity and in the detection of signals of natural selection down to individual bases. The B10K Project began with the Avian Phylogenomics Consortium (phase I), which analysed 48 genomes from representatives of most bird orders  9, 10. Here we report the genome sequencing outcomes from phase II of the project: these outcomes include a total of 363 species in 92.
We confirmed that this gene duplication occurred exclusively in Passeriformes (found in 161 out of 173 species; its absence in 12 species is caused by incomplete assemblies), resulting in a one-to-many relationship with the single copy in other birds (Extended Data Fig. 6 ). The synteny with surrounding genes identified the passeriform GH_L as the ancestral copy, and GH_S as a newly derived copy located in a different genomic context (Fig. 2a ). Moreover, when the pipeline was applied to both datasets (of 48 and 363 bird species), the higher taxon sampling allowed the detection of 439 additional orthologues with conserved synteny to chicken-many of which were lineage-specific gene copies. These additional orthologues, improved by the denser representation of species and the Cactus alignment, will drive downstream comparative analyses.
8a ). The latter use rapid changes in the diameter of the upper oesophagus to tune their vocal tract to the fundamental frequency of their song  26. The absence of CRNN might correspond to changes in visco-elastic properties of the oesophageal epithelium, and the loss of this gene may have contributed to the evolution of the diverse pure-tone vocalizations of songbirds (Extended Data Fig. 8b ). We next explored avian conserved sequences, genomic regions that evolve at a substantially slower substitution rate than expected under neutral evolution. Conserved sequences are often indicators of purifying selection  27  and are therefore useful for investigating function within the genome  28. To identify and measure conserved regions, we created conservation scores for each base pair of the 363-species Cactus alignment projected onto the chicken genome.
3% within the 77-way alignment and the 18. 6% within the 53-way alignment (Fig. 3c ). Furthermore, the increase was proportionally much larger in functional non-coding regions of the genome, including bases within long non-coding RNAs (lncRNAs) (16. 2% versus 4. 8% and 3. 2%), untranslated exons (30. 1% versus 8. 8% and 6. 0%) (Fig. 3c ), and other regulatory regions such as transcription factor binding sites (51. 2% versus 9. 7% and 6. 9%) (Fig. 3d ). Taken together, our results suggest that although functional non-coding regions are more plastic and less strongly conserved than coding regions, much of their sequence is under a higher degree of selective constraint than previously realized with sampling using fewer taxa. Overall, our dataset establishes birds as a system with unparalleled genomic resources.
Sample selection, DNA extraction, sequencing and assembly A total of 363 species from 218 families were included. The 363 genomes came from 4 data sources, and included 267 newly sequenced genomes and 96 publicly available genomes (Extended Data Fig. 1a , Supplementary Table  1 ). A total of 236 genomes were sequenced specifically for this project, drawing on samples from 17 scientific collections. The 3 largest contributors were the National Museum of Natural History of the Smithsonian Institution (140 species), Louisiana State University Museum of Natural Science (31 species) and Southern Cross University (23 species). According to the tissue type, we used different commercial extraction kits following the manufacturers' guidelines. B10K Project genomes were sequenced at BGI using the short-read sequencing strategy and assembled with SOAPdenovo v.
Annotation of repeat and protein-coding genes Tandem repeats were identified by Repeats Finder v. 4. 07b  42  and transposable elements were annotated using both homology-based (Repeat-Masker v. open-4. 0. 7  43  ) and de novo (RepeatModeler v. open-1-0-8  44  ) approaches. The ancestral state of total transposable elements was reconstructed with maximum likelihood using the fastAnc function in the R package phytools v. 0. 7-20  45. Annotation of protein-coding genes across the 363 bird genomes was conducted with a homology-based method using a primary reference gene set containing 20,194 avian genes (Supplementary Table  7 ). These annotations were then supplemented by non-redundant annotations from the 20,169 human gene set and the 5,257 transcriptomes set (Supplementary Information, Supplementary Table  8 ).
Cactus whole-genome alignment We ran Cactus (at commit f88f23d) on the Amazon Web Services (AWS) cloud, using the AWSJobStore of Toil to store intermediate files. We generated a phylogenetic hypothesis to use as a guide tree for Cactus by extracting ultraconserved element regions  46  from each of the 363 bird assemblies following a standard protocol  47  (https://phyluce. readthedocs. io/en/latest/tutorial-three. html) and performed maximum likelihood inference on the concatenated dataset using ExaML v. 3. 0. 9  48 on an high-performance computing system, assuming a general time-reversible model of rate substitution, γ-distributed rates among sites and five tree searches (Supplementary Information). We used an auto-scaling cluster, which varied in size during the course of the alignment but used a combination of c3.
Many-to-many orthologues represent situations in which multiple orthologues are found in both species. In one-to-many and many-to-many orthologues, the gene copy that is located in a specific genomic context by synteny is identified as the ancestral copy. In one-to-many and many-to-many orthologues, the gene copy that is out of the genomic context (no synteny) is considered as the duplicated gene copy. We identified orthologues using a synteny-based orthologue assignment approach that built on the whole-genome alignment generated by Cactus and synteny evidence (Extended Data Fig. 5a ). All potential homologous groups were captured from the Cactus alignment without specifying a reference genome. To gain insight into the relationships among different copies of one-to-many and many-to-many orthologue groups, we further applied the synteny evidence to distinguish the ancestral and novel copy, using the following steps.
Selection analysis on whole-genome alignments Neutral model. To estimate the degree of conservation or acceleration within a column requires evaluating the departure from a 'neutral' rate of evolution. This rate is described using a neutral model. We estimated a neutral model on the basis of ancestral repeats using an automatic pipeline (https://github. com/ComparativeGenomicsToolkit/ neutral-model-estimator). We extracted the ancestral genome from the alignment representing the MRCA of all birds, and ran RepeatMasker  43  to find avian repeats present in that genome (using the species library 'aves' from RepBase v. 20170127  55  ). A random sample of 100,000 bases within these repeats was used to extract a MAF, which was used as input to the phyloFit program from the PHAST v. 1. 5  56 package to create the neutral model (using a general reversible model of nucleotide substitution).
The PHAST framework allows only at most a single entry per genome per column, whereas the output MAFs may contain alignments to multiple copies. To resolve this, maf_stream (https://github. com/ joelarmstrong/maf_stream) was used to combine multiple entries per genome into a single entry (using maf_stream dup_merge consensus). Sex-determining chromosomes are known to evolve at a different rate than autosomes (the fast-X and fast-Z hypothesis)  10, 29, 57. Furthermore, micro-and macro-chromosomes in birds have been shown to evolve at different rates as well  10, 30. To remove any potential differences in neutral nucleotide substitution rates among these chromosomes as a factor, we generated a second set of neutral models that represent the neutral rate on these three chromosome sets (we call this set the 'three-rate model').
Any site that had a q value less than 0. 05 was deemed significantly conserved or accelerated; Extended Data Fig. 9a  provides the proportions of accelerated and conserved regions. We extracted the significantly accelerated and conserved sites from the PhyloP wiggle files using the Wiggletools v. 1. 2. 3  59  command wiggletools gt <threshold> abs, with the appropriate score threshold from Supplementary Table  15. Intersection with functional regions of the genome. We split Ref-Seq genes (obtained via the RefSeq gene track on the galGal4 UCSC browser  31  ) into sets of coding exons, UTR exons and introns. We also downloaded a lncRNA gene set from NONCODE v. 5  60  to obtain lncRNA regions and mapped all ancestral repeats from the root genome (as described in 'Neutral model') to chicken to get ancestral-repeat regions.
All of these regions were made mutually exclusive by removing overlaps with all other region types. Finally, 100,000 bases were randomly sampled from each of these mutually exclusive regions and used to extract a corresponding distribution of scores for each region from the wiggle file. We identified transcription factor binding motifs on the basis of the chicken genome using JASPAR  61. The results are shown in Fig. 3c, d , Extended Data Figs. 9b, 10a. Distribution of rate of alignment columns. Finding the distribution of rates of alignment columns (relative to the neutral rate) is necessary for determining the strength of conservation that is needed for significance. We sampled 10,000 sites at random from each of the galGal4 (for the 363-way alignment) and galGal6 (for the 77-way alignment) assemblies.
For the 363-way alignment, a MAF was exported containing the columns for each of these sites using hal2maf, and for the 77-way alignment the mafFrags program was used to obtain the columns from the UCSC browser database. The --base-by-base mode of PhyloP was used to obtain the 'scale' parameter for each column, which represents a best-fit multiplier of the neutral model applied to all branch lengths in the tree. For the 363-way alignment, we divided the columns within the MAF into three separate files according to their bin within the three-rate model, and used the appropriate model for each resulting MAF. The results are shown in Fig. 3b , Extended Data Fig. 10b. Realignment of conserved sites. Our conservation and acceleration calls fundamentally rely on information from the alignment. For this reason, errors in the alignment could potentially cause erroneous acceleration or conservation calls.
Acknowledgements The  B10K  Project would not be possible without the efforts of field collectors, curators and staff at the institutions listed in Supplementary Table 1. We thank  J. Klicka  ( Burke Museum ),  J. B. Kristensen  ( Natural History Museum of Denmark ),  A. T. Peterson  ( Biodiversity Institute of the University of Kansas ),  M. B. Robbins  ( Biodiversity Institute of the University of Kansas ),  F. Robertson  ( University of Otago ),  T. King  ( University of Otago ), K. C. Rowe ( Museums Victoria ),  K. Winker  ( University of Alaska Museum ) and the late  A. Baker  ( Royal Ontario Museum ) for providing tissue samples;  B. J. Novak  for sample coordination;  Dovetail Genomics  for the assembly of Caloenas nicobarica;  T. Riede  for helpful discussions of the mechanism and evolution of the vocal tract filter in songbirds; and  China National Genebank at BGI  for contributing to the sequencing for the  B10K  Project.