dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomic coverage Description: The higher taxonomy of specimens was determined from the GBIF backbone taxonomy when those data came from GBIF. For the data that did not originate from GBIF, we matched the family (B specimens) or genus (H specimens) in the data to the backbone (GBIF Secretariat 2017). Only seven specimens could not be matched to the backbone. Two had no identification and the other five were homonyms at the genus level (e. g. Pellaea, which is both an animal and plant genus). More than 90% of specimens were, not unexpectedly, Tracheophyta, but within this phylum, there was a significant taxonomic coverage of 204 different families in 58 different orders (Fig. 3 , Suppl. material 1). Although we aimed at incorporating 25% nomenclatural type specimens within the dataset, according to their data, only 19% are types.
This lower value is because some collections are not created primarily for taxonomy and they therefore do not hold many types. Non-type specimens were selected as specimens without any type status. Hence, some specimens listed as non-types could actually be types, if they had not been identified as such in their digital publication. Regarding the specimen collector names, there are 1,170 different names associated with the dataset. However, it is likely there are duplicates amongst those 1,170, as some names will not be exact textual matches. Only 6% of the specimens had no collector information.
Data set name: Benchmark Dataset Description: This landing page contains a CSV file compiling all data associated with herbarium specimens that are part of this dataset, as they could be found on GBIF, JACQ or FinBIF. In addition, DOIs of the individual specimens uploaded to Zenodo and direct links to the different files (JPEG, TIFF, JSON, PNG) are also included. Index of these added variables: -persistentID: Persistent Identifier of the collection specimen. The persistent identifier is maintained by each institution and should always lead to the most up-to-date version of a digital specimen record. Apart from the persistent identifier, other data are liable to being amended in institutional databases. Data uploaded as part of this dataset will not be updated with changes at the collection's repository, but this persistent URI will always point to the up-to-date information in the institutional system.
-jpegURL, tiffURL, jsonURL: URLs pointing straight to the respective image and data files themselves, to facilitate (selective) batch downloads. -pngSegAllURL and pngSegSelURL: Segmented overlays of the herbarium specimens indicating the location of different labels and reference material on the sheet ("All") and their content ("Sel"). More information can be found in the paper (in prep. ) associated with this data publication and the individual depositions themselves. -DOI: The DOI of the deposition of images and data of these specimens on Zenodo. DOIs point to the most up-to-date version of these depositions at the time of the publication of this CSV file. As a rule, this CSV file will be updated should any changes happen to any of the depositions. -jpegURL2, tiffURL2: A few herbarium sheets had labels on the back and consisted therefore of two scans.
eu). Currently, the main method being used is human transcription, either using professional transcribers or volunteers. Professionals may be herbarium employees or outsourcing companies and they may work on bespoke IT systems or online. Volunteers are often recruited online through citizen science portals, such as Notes from Nature (http s://www. notesfromnature. org), Les Herbonautes (http://lesherbonautes. mnhn. fr), DoeDat (h ttps://www. doedat. be) and DigiVol (https://digivol. ala. org. au/). These different methods and platforms vary considerably in their approaches to quality control and completeness of transcription. There are many unresolved questions about the success of these different approaches, the quality of the data they generate and their cost-effectiveness  (Ellwood et al.
2015). Such questions of quality can be addressed with a benchmark dataset of images, such as the one described in this paper. Trials involving this dataset on the transcription platforms mentioned above are already underway and some had already finished at the time of this article's submission. A publication including a comparatory analysis is planned. Another approach to data extraction is automation. This might involve optical character recognition of text or other forms of pattern recognition  (Drinkwater et al. 2014). Various research groups have had some success in this  (Haston et al. 2015 ). Yet, questions remain on the quality of the data output, including post-processing to classify and standardise the data obtained and how this compares to human transcription. Again, benchmark datasets are required to provide a comparison of techniques.
Though, even this diverse set of specimens will not provide training data for every possible need, particularly considering the wide range of languages used on specimens. The subset of 250 image overlays indicating the location and nature of the sheet labels can be used to evaluate the impact of segmenting out the labels before automated data extraction. It can also serve to train algorithms designed to automate label recognition. Digital images of herbarium specimens may also be used for other purposes, for example, to extract trait data from plants or to identify the species in question  (Cope et al. 2012 , Carranza-Rojas et al. 2017 ). However, techniques and software need to be developed to industrialise this to the scale required. It can be expected that techniques of artificial intelligence / machine learning (ML) might be applied for this and the dataset to be used for training and proving purposes  (Wäldchen et al.
2018 ). Some analysis techniques may only be suitable for certain types of specimen, for example, when ML algorithms are trained only in one language or the handwriting of one collector. Here, we have provided a wide variety of test images from which subsamples can be selected for different purposes. However, in selecting the images, we have not attempted to provide a random subsample of specimens, but have tried to provide a good cross-section of the different kinds. This means that some countries, languages and scripts are not represented at all in the collection and the collection will be biased geographically and taxonomically. However, for those countries and languages represented in the set, there will be multiple specimens. The whole dataset has been archived to the Zenodo research data repository (https:// zenodo.
org, Suppl. material 7), where each specimen has its own digital object identifier (DOI). This DOI resolves to a landing page on Zenodo, which contains the specimen's currently available data as a JSON-LD file and the scanned image in a compressed JPEG format. If available, a lossless TIFF version of the image and two overlay PNG versions can be found there as well. Most data values have also been incorporated into the Zenodo database to improve findability. They are encoded in the "Subjects" fields, combined with persistent identifiers for the Darwin Core and Dublin Core terminology. A landing page with its own DOI for the dataset as a whole contains a CSV file that comprises all available metadata for each specimen and links to the JPEG, PNG and TIFF files. This overarching file should make it easy to download parts of the dataset, such as JPEGs only, TIFFs only or even English specimens only, with simple batch download scripts.
Acknowledgements We gratefully acknowledge all the collectors of the specimens in our test dataset. We also wish to thank  Nuno Veríssimo Pereira  for identifying the language(s) used on the labels of each specimen and  Phillippe Hendricx  for creating the segmented overlays. We thank curators, technical collection staff and the management of the herbaria that provided digitised specimens for their support. Funding for this project was received from the  EC H2020  programme (ICEDIG:  RIA 777483 ).
The crl5 and smos1-3 mutants  (Kitomi et al. , 2011; Aya et al. , 2014)  were grown in a greenhouse in Montpellier, France, in October 2017 under short day conditions (11 h light-13 h dark). At least 18 panicles were used for panicle phenotyping. All greenhouse plants were grown at 28 °C with 80% relative humidity. For phenotyping analyses, each panicle was spread out and fixed on white paper using adhesive tape. Panicles were photographed and the images were used for panicle structure and seed number analysis with P-TRAP software  (AL-Tam et al. , 2013).
Data analysis We trimmed reads and removed adaptors with cutadapt  (Martin, 2011) , before mapping to the MSU v7 annotation of the Oryza sativa ssp. japonica cv. Nipponbare reference genome  (Ouyang et al. , 2007)  using STAR in two-pass mode  (Dobin et al. , 2013). To generate per-library gene expression cutoffs, we used the 95th percentile of reads that mapped to intergenic regions of the genome, as described previously  (Harrop et al. , 2016). We used DESeq2  (Love et al. , 2014)  for differential expression analysis of genes that passed the cutoff. We used annotations from PlnTFDB v3. 0  (Pérez-Rodríguez et al. , 2010)  and PlantTFDB v4. 0  (Jin et al. , 2017)  to analyse expression of transcription factors. Soft clustering of transcription factor genes was performed with Mfuzz  (Kumar and Futschik, 2007) , and enrichment of transcription factor family genes was tested with the GSEA method using the FGSEA package  (Subramanian et al.
Reproducibility and data availability Raw sequence data are hosted at the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) under accession PRJNA518559. The code we used to analyse the RNAseq data and panicle phenotype data is hosted at https://github. com/tomharrop/5acc, and the code for qPCR analysis is at https://github. com/othomantegazza/5accqpcr. We used snakemake  (Köster and Rahmann, 2012)  to arrange analysis steps into workflows and monitor dependencies, and Singularity  (Kurtzer et al. , 2017)  to capture the computing environment. The final results and all intermediate steps can be exactly reproduced from the raw data with a single command using snakemake and Singularity. The source for this manuscript is hosted at https://github. com/tomharrop/ird-5acc-paper.
Parallel changes in panicle architecture between wild and domesticated accessions To measure the diversity of panicle architecture, we phenotyped 91 rice accessions (Supplementary Table  S1 ), including wild Asian rice (Oryza rufipogon), domesticated Asian rice (Oryza sativa), wild African rice (Oryza barthii), and domesticated African rice (Oryza glaberrima), using P-TRAP image analysis software for automated measurement of traits (Fig. 1A ; Supplementary Fig. S1 ; Supplementary Tables  S1, S3 ;  AL-Tam et al. , 2013). Principal components analysis (PCA) of the phenotyping data identified a major coordinate (PC1) that accounts for 46. 5% of variability (Fig. 1B ). PC1 separates domesticated and wild accessions, but not Asian and African accessions, and is the only component that separates panicles from different accessions (Supplementary Fig.
AP2/EREBP-like genes that were more highly expressed in IM mainly belong to the ERF and DREB clades. In contrast, most MADS-box genes that pass the cutoff were more highly expressed at the DM stage. Clades for AP2/EREBP-like genes are from the Plant Transcription Factor Database v4. 0  (Jin et al. , 2017)  and  Sharoni et al. (2011). MADS-box clades were manually tabulated from  Arora et al. (2007). IM DM O. rufipogon O. sativa indica O. barthii O. glaberrima O. rufipogon O. sativa indica O. barthii O. glaberrima ERF85 LOC_Os05g37640 ERF3 LOC_Os02g10760 FZP LOC_Os07g47330 AP2/EREBP85 LOC_Os03g06920 ERF74 LOC_Os05g41780 ERF48 LOC_Os08g31580 ERF64 LOC_Os03g08500 AP2/EREBP77 LOC_Os01g04750 DREB1B LOC_Os09g35010 ERF69 LOC_Os03g08490 ERF103 LOC_Os02g52670 ERF37 LOC_Os04g46410 ERF18 LOC_Os10g38000 DREB1D LOC_Os06g06970 AP2/EREBP96 LOC_Os05g47650 ERF96 LOC_Os10g41330 ERF136 LOC_Os07g22730 AP2/EREBP79 LOC_Os01g59780 DREB1C LOC_Os06g03670 PLT10 LOC_Os03g07940 ERF33 LOC_Os04g46400 ERF47 LOC_Os03g09170 SUB1B LOC_Os09g11480 DREB1A LOC_Os09g35030 ERF133 LOC_Os09g35020 DREB1G LOC_Os02g45450 ERF91 LOC_Os02g43790 DREB1E LOC_Os04g48350 ERF104 LOC_Os08g36920 ERF112 LOC_Os12g41060 ERF77 LOC_Os04g52090 SHAT1 LOC_Os04g55560 ERF93 LOC_Os04g46220 AP2/EREBP129 LOC_Os01g04800 ERF32 LOC_Os02g43940 ERF34 LOC_Os04g46440 ERF76 LOC_Os04g57340.
AP2/EREBP-like panicles with a shorter rachis with fewer primary branches (Fig. 4 ; Supplementary Table  S7 ), consistent with a peak of PLT8 expression in rachis meristem tissues from O. sativa ssp. japonica (Supplementary Fig. S6 ;  Harrop et al. , 2016). Panicles from the small organ size1 (smos1) mutant of ERF142  (Aya et al. , 2014)  have a reduced number of primary and secondary branches, and fewer spikelets (Fig. 4 ; Supplementary Table  S7 ). ERF142 expression is highest in primary branch and elongating primary branch meristem tissues in O. sativa ssp. japonica (Supplementary Fig. S6 ;  Harrop et al. , 2016). Although neither gene was differentially expressed in our RNAseq dataset, these phenotypes support the involvement of AP2/EREBP-like genes in control of panicle architecture.
AP2/EREBP-like gene expression is associated with panicle diversity and domestication To identify common patterns of expression of transcriptional regulators related to variation in panicle phenotypes, we used soft clustering of scaled L 2 FCs between IM and DM on the subset of annotated TF genes that were detected in our RNAseq dataset. We recovered six clusters comprising a total of 119 genes (Fig. 5 ; Supplementary Table  S8 ). To determine which clusters were related to domestication, we calculated correlations between the mean L 2 FC value of genes in each cluster and PC1 in the phenotyping data, and between mean L 2 FC and the number of secondary branches and spikelets from repeat panicle phenotyping for the accessions used for RNAseq (Figs 1, 5B; Supplementary Fig. S7 ). Clusters 3, 4, and 6 correlated with spikelet number (SpN) and secondary branch number (SBN), but not with PC1, meaning that the L 2 FC of genes in those clusters does not correlate with the phenotypic differences between wild and domesticated accessions.
Cluster 2 also had the strongest negative correlation with PC1, and low L 2 FCs in O. sativa ssp. indica. The lower L 2 FCs in domesticated accessions could implicate cluster 2 genes in promotion of determinate meristem fate, because their lower expression at the DM stage may result in more activity of indeterminate axillary meristems. Eight GROWTH-REGULATING FACTOR1 (GRF) family genes, which are involved in the regulation of cell proliferation  (Kim and Tsukaya, 2015) , were detected in our dataset, and three of them were present in cluster 2 (P adj =7. 6×10 -3 , hypergeometric test). In contrast to cluster 2, most of the genes in cluster 5 have L 2 FCs close to zero in O. sativa ssp. indica, and negative L 2 FCs in the other accessions (Supplementary Fig. S8 ), meaning that the expression of these genes decreases at the DM stage in all accessions except O.
, 2017; Fujishiro et al. , 2018; Huang et al. , 2018). Our analysis indicates a modified expression pattern of some AP2/EREBP-like genes between wild and domesticated rice accessions. One of the clusters of genes with expression patterns that correlate to the main phenotypic differences between wild and domesticated accessions contained an enrichment of AP2/EREBP-like genes. In addition, nine out of the 19 genes with a stage×domestication interaction across the entire dataset were AP2/EREBP-like genes (Fig. 6A ). Eight of these nine genes have lower expression in panicles at the DM stage, and may be involved in promotion of indeterminate axillary meristem and/or suppression of the transition from axillary meristem to spikelet meristem. Among these genes, ERF130 (MULTI-FLORET SPIKELET1, MFS1) regulates the timing of the transition of spikelet meristems to terminal spikelets and positively regulates the expression of OsIDS1 and SNB  (Ren et al.
Acknowledgements Dr Ko Hirano  and  Prof. Yoshiaki Inukai  from  Nagoya University, Japan , kindly provided smos1-3 and crl5 mutant seeds, respectively. We thank  Lady Johanna Arbelaez Rivera  ( CIAT, Colombia ) for plant care and  Céline Cardi  and  Hélène Vignes  ( Grand plateau technique régional de génotypage, CIRAD, Montpellier ) for support with high-throughput qPCR and cDNA library preparation. Sophie Chéron  and  Harold Chrestin  assisted with plant care and phenotyping, and  Christine Tranchant-Dubreuil ,  Axel Verdier , and the  IRD  bioinformatics platform assisted with technical support in Montpellier. We also thank  James Tregear  for feedback on the manuscript and  Alain Ghesquière  for discussion on the project.
RNAseq quality check and assembly Data quality of the sequencing files was checked using the FastQC software  [14]. For each strain, de novo transcriptome was reconstructed using Trinity (release 2014-07-17;  [15] ) with the '-trimmomatic -normalize_reads -normalize_by_read_set' options. Weakly expressed transcripts (isoform percentage < 1 and fragment per kilobase of transcript per million mapped reads (FPKM) < 1) were removed from the dataset with the RSEM tool  [16]  included in the Trinity package. For each Trinity-predicted "gene" only the most abundant isoform was conserved in the final dataset.
Transcript annotation Eukaryotic and prokaryotic ribosomal RNA were screened with Rnammer (v. 1. 2; [17] ). Transcript open reading frames (ORFs) were predicted using Transdecoder (r2014-07-04, available at http://transdecoder. github. io), protein domains were predicted with HMMER (v. 3. 1b1, [18] ) based on the PFAM-A database  [19] , transmembrane helices were identified with tmhmm (v. 2. 0,  [20] ), signal peptide cleavage sites were predicted with SignalP (v. 4. 1,  [21] ). All predicted transcripts and predicted proteins were identified using respectively BLASTx and BLASTp  [22]  against Swissprot-Uniprot (r2017-01-18) and Uniref90 (r201611-02) databases and against the filtered predicted proteomes of M. circinelloides CBS 277. 49 (v2,  [11] ), Rhizopus oryzae 99-880  [23]  and Phycomyces blackesleeanus NRRL1555 (v2,  [11] ) available on the Joint Genome Institute (JGI) database (genome.
jgi. doe. gov/). An Evalue cut-off of 1e-4 was used for all BLASTp and BLASTx analysis, only the best match per database and protein was conserved. Putative functions were assigned to predicted proteins using the Gene Ontology (GO) database  [24]  and the Enzyme Commission number (EC) classification  [25]. Predicted proteins were assigned to GO categories by transferring the annotation of BLAST matches and protein domains. EC numbers were assigned by profile search with PRIAM (r2015-03-04,  [26] ) and transferred from the predicted proteomes BLAST matches.
Transcriptome quality check In order to assess the quality of the transcriptomes, statistics such as percentage of realigned reads and N50 were calculated. Transcriptome completeness in terms of gene content was assessed by searching with BUSCO (Benchmarking Universal Single-Copy Orthologs, v2, http://busco. ezlab. org/, [27] ) for the presence/absence of the conserved eukaryotic orthologous genes available in the BUSCO software.
Comparative transcriptomics Significant differences in GO category occurrence among species were identified with an internal script using the Fisher's exact test (pvalue < 0. 05, available on github). All EC numbers for each species were mapped onto metabolic networks with iPath2  [28]. When a species specific EC number was found, the transcript annotation was checked manually. All predicted proteomes were compared against each other based on sequence similarity to identify orthologous proteins using the software Orthofinder v. 1. 1. 2 [29]  (E-value 1e-2, inflation 1. 5). Orthologous proteins were clustered according to the reported lifestyle of the producing fungal species. Clusters were then compared: cheese/non-cheese, pathogen/nonpathogen and core transcriptome/non-endophyte (orthogroups composed of proteins of all species except ME).
Phylogenomic analysis Single copy orthologs shared by the five studied Mucor spp. as well as Rhizopus oryzae strain 99-880 and Phycomyces blackesleeanus strain NRRL1555 (both later species being considered as outgroups) were kept for phylogenetic reconstruction. For each of the 1289 obtained clusters, a multiple alignment was inferred using PRANK v. 170427  [30] , run with default settings. Spuriously aligned regions were excluded with trimAl v1. 4. r15  [31]  with a 0. 2 gap threshold. Subsequent alignments were concatenated in a supermatrix of 727479 sites. This matrix was used to reconstruct species tree by maximum likelihood inference and by Bayesian Monte Carlo Markov Chain (MCMC) samples. RAxML PTHREADS v. 8. 2. 9  [32] , a program for Maximum Likelihood based inference, was used with a partitioned WAG+G model, where each data partition represented a single input gene family.
Investigation of specific gene families Transcripts involved in secondary metabolism (PKS and NRPS), sugar transport, aminoacyl degradation (decarboxylases, transaminases and deaminase), fatty acid degradation (alkaline and acidic lipases) and virulence (M. circinelloides CBS 277-49 ferroxidases fet3 and ID112092, Rhizopus delmar fob, FTR1 and cotH) were identified in each transcriptome. PKs and NRPs transcripts were identified using the genes annotated in the Mucor circinelloides CBS277. 49 genome available at the JGI. Sugar transporters, cotH transcripts and FTR1 transcripts were identified using the corresponding domain profiles: respectively Pfam ID PF00083, PF08757 and PF03239. Alkaline and acidic lipases and sequences of genes involved in virulence were collected from the NCBI database and used to search the Mucor predicted proteomes using diamond BLASTp  [34].
RNA sequencing, transcriptome assembly and annotation Sequencing of the five Mucor transcriptomes resulted in 25 to 35 million pairs of 101 base paired-end reads (Table  2 ). Reads were assembled into 16,950 to 21,556 filtered transcripts grouped into 13,655 to 15,554 Trinity "genes". Except for ML, assemblies correctly represented the read sets. Indeed, the percentage of realigned reads was above 93% for all species except ML which was 70% (73% for the transcriptome including all isoforms). Except for ME, the average transcript length was close to 1,200 bp for all species. In addition, more than 97% of single-copy orthologs were complete reflecting the high-quality of the assemblies. The assembly of ME was of lower quality as shown by the N50 of 1,292bp, a higher number of predicted genes and the identification of single-copy orthologs against the BUSCO eukaryotic database of which 67% were complete, 20% fragmented and 13% missing.
ACKNOWLEDGMENTS The authors are thankful to  Dr Antoine Hermet  for his involvement in the early steps of this project. We also thank  Nantes BiogenOuest  " Nantes Genomics Core Facility " for technological core facilities. This research was funded by the the  Région Bretagne (ARED program ) and EQUASA, a technological platform of the  Université de Bretagne Occidentale ,  16  in the framework of the  MUCORSCOPE  project. The authors thank the  Roscoff Bioinformatic platform: ABiMS  (http://abims. sb-roscoff. fr)for providing computational resources. The authors are also grateful to the reviewers for their helpful comments.