dataesr/scientific-paragraphs-categorization huggingface dataset:
30 e main objective is to ensure the accessibility, integrity, readability and comprehensibility of the data for as long as necessary, by adapting the level of requirements necessary for the archiving as appropriate for the duration in question. In this framework, the CINES lobbies the players on the market to ensure that their file formats will remain readable for many years. A team of engineers is engaged in a permanent race against obsolescence by ensuring that software and hardware platforms can continuously access data. The FACILE web service 31 lists the range of formats currently supported by the CINES. Accreditation is issued by the Ministry of Health for a duration of three years and is renewable. 39 Seals. The ASIP Santé also issues seals, 40 which are issued as explained in the following diagram: 41 Observations.
NOTES  Furthermore, Article 38 of the Digital Republic Act stipulates that "A decree lays down the conditions under which the exploration of texts and data is implemented, as well as the terms for storage and communication of the files produced on conclusion of the research activities for which they were produced"; The above discussion of the concept of TDM and the issues surrounding it, together with the observation of organisational models already existing in France, in the H2020 programme and in other countries, can help refine a proposal for a framework for the practice of TDM and, more broadly, Open Science. This procedure for assessing digital data curation entities should ensure that they abide by their obligations, in particular as regards: data security; compliance with standards and data formats; compliance with obligations relating to technical infrastructures and those forming networks (maintenance of platforms for the collection and provision of data, maintenance of hubs for network management); provision of good behaviour guides and an Ethics Charter.
Conceptualising a typical digital data curation entity There are already bodies in France that curate digital data and make it available: the Strasbourg Astronomical Data Centre, and Huma-Num for data in the human and social sciences. In order to conceptualise a typical digital data curation entity, we propose undertaking a precise analysis of these existing data repositories, especially in terms of structure, security, power, capacity, staff, features, use by the scientific community, and indicators.
direct marketing: in this area, TDM techniques are used, for example, to segment customer databases and predict customers' purchasing intentions in order to optimise the marketing pitch; communication: anti-spam filtering of emails and the global Echelon system for the interception of private and public communications (SIGINT) developed by the United States, the United Kingdom, Canada, Australia and New Zealand in the framework of the UKUSA Agreement, both use TDM techniques; banking and finance; insurance and health; medical and pharmaceuticals.
Article 3 -Text and data mining 1. Member States shall provide for an exception to the rights provided for in Article 2 of Directive 2001/29/EC, Articles 5(a) and 7(1) of Directive 96/9/EC and Article11(1)of this Directive for reproductions and extractions made by research organisations in order to carry out text and data mining of works or other subjectmatter to which they have lawful access for the purposes of scientific research. 2. Any contractual provision contrary to the exception provided for in paragraph 1 shall be unenforceable. 3. Rightholders shall be allowed to apply measures to ensure the security and integrity of the networks and databases where the works or other subject-matter are hosted. Such measures shall not go beyond what is necessary to achieve that objective.
Article 3 -Text and data mining 1. Member States shall provide for an exception to the rights provided for in Article 2 of Directive 2001/29/EC, Articles 5(a) and 7(1) of Directive 96/9/EC and Article11(1)of this Directive for reproductions and extractions made by research organisations in order to carry out text and data mining of works or other subjectmatter to which they have lawful access for the purposes of scientific research. 2. Any contractual provision contrary to the exception provided for in paragraph 1 shall be unenforceable. 3. Rightholders shall be allowed to apply measures to ensure the security and integrity of the networks and databases where the works or other subject-matter are hosted. Such measures shall not go beyond what is necessary to achieve that objective. 4. Member States shall encourage rightholders and research organisations to define commonly-agreed best practices concerning the application of the measures referred to in paragraph 3.
This networking and the construction of computer hubs between the digital data curation entities of each scientific discipline implies the definition of standard formats and decisions on how to ensure the interoperability of the data. NOTES 1. Florence Millerand, "La science en réseau. Les gestionnaires d'information 'invisibles' dans la production d'une base de données scientifiques" (Networked science: The 'invisible' information managers in the production of a scientific database), Revue d'anthropologie des connaissances, 6(1), 2012, pp. 163-190. • • • • •.
oeni genomes available in databases was used to delineate 11 groups of phylogenetically related strains (see Fig. S1  in the supplemental material). They were named groups A and B, according to previous studies  (27, 31) , and subgroups A1 to A6 and B1 to B3 (Fig. S1 ). A comparative genomic analysis revealed 11 to 1,695 SNPs specific for each of the 11 groups (see Table  S1  in the supplemental material). A total of 40 SNPs were manually selected, with 2 to 6 SNPs specific for each group of strains, except for subgroup B2 for which no SNP was retained (see Materials and Methods). Concatenation of the 40 selected SNPs generated 11 sequence types (STs), corresponding to each of the 10 groups, plus strain C52 (group N), which does not belong to either group A or group B (28, 31) (see Table  S2  in the supplemental material).
Both PCRs were performed under the same conditions in a thermocycler (T_100; Bio-Rad, France) with the following program: 95°C for 15 min, followed by 30 cycles of 30 s at 94°C, 90 s at 62°C, and 90 s at 72°C, and a final step of 30 min at 60°C. Then PCR products M1 and M2 were diluted 40 and 60 times, respectively, and pooled. Next, 2 l of the mixture was added to 9 l of highly deionized (HI-DI) formamide (Applied Biosystems) and analyzed by MWG Eurofins Operon (Cochin Institute, France). The genotyping results were processed with GenMarker (SoftGenetics) software in which a specific MLVA panel has been incorporated in order to automatically determine the number of repetitions of each TR. The combination of the number of repetitions of TR1 to TR5 represents the digital profile of a colony.
All of the MLVA profiles were then integrated in a database of BioNumerics v5. 1 (Applied Maths, Belgium) software, and a number was affiliated with each different profile to facilitate the analysis. A minimum spanning tree was then calculated by ranking the variables of each TR and profile number by category (calculate minimum spanning tree, coefficient: categorical). 16S rRNA gene analysis. Colony PCRs were performed in 20-l reaction mixtures containing 10 pmol each of universal primers BSF8 (5=-AGAGTTTGATCCTGGCTCAG-3=) and BSR1541 (5=-AAGGAGGTG ATCCAGCCGCA-3=), 4 l of Taq 5ϫ MasterMix (New England BioLabs), and 2 l of cell lysate. The PCR program was 95°C for 5 min, 30 cycles of 30 s at 95°C, 30 s at 55°C, and 30 s at 72°C, and a final step of 10 min at 72°C. PCR products were sequenced on both DNA strands (MWG Operon, Ebersberg, Germany), and species identification was achieved using BLAST (https://blast.
ncbi. nlm. nih. gov/Blast. cgi). Pielou and Shannon-Weaver diversity indexes. Shannon-Weaver and Pielou diversity indexes and rarefaction curves were calculated using EstimateS 9. 1. 0 software  (46). These two indexes are complementary and make it possible to assess the diversity of O. oeni strains and the evenness of their distribution across the regions studied. Classification of strains in phylogroups using SNP genotyping. A method for strain classification by SNP genotyping was developed to assign the newly identified strains of O. oeni to phylogenetic groups A and B and their respective subgroups previously reported in Campbell-Sills et al. (31). The 49 genomes reported by Campbell-Sills et al. (31)  were mapped against the genome of strain PSU-1 and revealed a whole set of 6,120 SNPs that discriminate strains from the different subgroups of A and B strains with 100% accuracy (Table  S1 ).
A manual curation and selection process was performed to select 40 SNPs that could be amplified and sequenced by the Sequenom strategy. In order to amplify their genomic regions, we designed multiplex PCRs of primers with the software Suite 1. 0 Assay Design (Sequenom Inc. , San Diego, CA). Genotyping of the collected strains was performed using the iPLEX GOLD kit in the MassArray facility (Sequenom). The extension products were spotted onto a SpectroCHIP and analyzed by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF). The Sequenom analysis was performed using TYPER 4. 0 (Sequenom). The assignment of alleles was done in real time using SpectroCALLER, and then the results were displayed on SpectroACQUIRE (Sequenom). The genotyping results for the 40 SNPs for each strain were concatenated into a single sequence of 40 bp.
The sequence alignments and phylogenetic analysis were performed with MEGA software 6. 0. 5  (47)  with 1,000 bootstraps using the neighbor-joining distance calculation with the Kimura 2 parameter. The data were also included in BioNumerics v5. 1 software (Applied Maths, Belgium). A similarity matrix was then calculated with the neighbor-joining clustering parameter, with a 100% open gap penalty for pairwise alignment, and an MST was built from this matrix.
ACKNOWLEDGMENTS We are grateful to  M. -C. Colosio ,  E. Vinsonneau ,  P. Cottereau , and  V. Gerbaux  from the  Institut Français du Vin ,  S. Becquet  from the  Syndicat des Vignerons Bio d'Aquitaine ,  R. Bauduin  from the  Institut Français de Production du Cidre , and  V. Pladeau  from  SudVinBio  and many wine estates for providing wine and cider samples. This study was supported in parts by the  European Commission  ( FP7-SME project Wildwine , grant agreement no. 315065 ) and the  French Ministry of Agriculture  (project  LevainsBio   CASDAR AAP-2012  no. 1220 ).
Each individual cell produces its own set of transcripts, which is the combined result of genetic variation, transcription regulation and post-transcriptional processing. Due to this combinatorial nature, obtaining the exhaustive set of full-length transcripts for a given species is a never-ending endeavor. Yet, each RNA deep sequencing experiment produces a variety of transcripts that depart from the reference transcriptome and should be properly identified. To address this challenge, we introduce a k-mer-based software protocol for capturing local RNA variation from a set of standard RNA-seq libraries, independently of a reference genome or transcriptome. Our software, called DE-kupl, analyzes k-mer contents and detects k-mers with differential abundance directly from the raw data files, prior to assembly or mapping.
This enables to retrieve the virtually complete set of unannotated variation lying in an RNA-seq dataset. This variation is subsequently assigned to biological events such as differential lincRNAs, antisense RNAs, splice and polyadenylation variants, introns, expressed repeats, and SNV-harboring or exogenous RNA. We applied DE-kupl to public RNA-seq datasets, including an Epythelial-Mensenchymal Transition model and different human tissues. DE-kupl identified abundant novel events and showed excellent reproducibility when applied to independent deep sequencing experiments. DE-kupl is a new paradigm for analyzing differential RNA-seq data with no preconception on target events, which can also provide fresh insights into existing RNA-seq repositories.
Results Reference datasets are an incomplete representation of actual transcriptomes First, we analyzed k-mer diversity in different human references and highthroughput experimental sequences. To this aim, we extracted all 31-nt k-mers from sequence files using the Jellyfish program  [14]. Figure  1A -B compares k-mers from Gencode transcripts, the human genome reference and RNA-seq libraries from 18 different individuals  [15]  corresponding to three primary tissues (6 libraries/tissue). To minimize the risk of including k-mers containing sequencing errors, we retained for each tissue only the set of k-mers that appear in 6 or more individuals. Measures of k-mer abundance show that k-mers are overwhelmingly associated to Gencode transcripts (Fig  1B1 ). However, when considering k-mer diversity, a large fraction of k-mers are tissue-specific and not found in the Gencode reference (Fig  1A ).
First, they may stem from genetic polymorphism in the studied sample. Second, they may result from RNA processing, notably, but not limited to, splicing and polyadenylation. A predominant source of k-mers in this category is intron retention, whose products are not usually incorporated into reference databases and are mostly by-products of regular gene expression. A third, major source of k-mer "innovation" is intergenic expression (eg. lincRNA, antisense RNA, expressed repeats or endogenous viral sequences). Altogether, the combination of these genetic, transcriptional and posttranscriptional events may have a profound impact on transcript function. A new k-mer based protocol for deriving transcriptome variation from RNA-seq data We designed the DE-kupl computational protocol with the aim to capture all k-mer variation in an input set of RNA-seq libraries.
Another stringent filter is the removal of k-mers matching reference Gencode transcripts. The rationale for this is that the bulk of k-mers in RNA-seq data comes from expressed exons, and we are not interested in this canonical exon expression, as it can be captured efficiently by conventional, reference-based protocols  [8, 9]. Discarding these k-mers enable us to ignore the overwhelming signal caused by known transcripts and focus on expressed regions harboring differences from the reference transcriptome. Two modes are available to perform differential analysis of k-mers (Figure  S10  & Methods): The t-test filter mode is fast and has lower sensitivity, i. e. it retrieves only the most significantly differentially expressed k-mers. The DESeq2-based mode  [19]  is slower, more sensitive and is recommended for small samples (fewer than 6 vs.
Assigning contigs to biological events We assigned DE contigs generated from the EMT dataset to 11 classes of potential biological events, using the rule set described in Table  3. Since intragenic DE contigs may result from a mere over/under-expression of their host gene and do not necessarily reflect a differential usage (DU) of transcript isoforms, we implemented a simple strategy to distinguish the two situations based on the differential status of the host gene (Methods). We made this distinction for splicing, polyadenylation, SNVs and intron retention (Table  3 ). From the total set of 133k DE contigs (supplemental data), we extracted about 6900 contigs matching our rule set for either event class (Table  3 ). We noted that a single event often generates multiple contigs. We thus further grouped contigs into "loci", defined as independent annotated genes or intergenic regions harboring one or more contigs (Table  3 ).
These antisense RNAs include very strong cases of differential expression (Fig 7D ), sometimes combined to apparent repression of the sense gene (Fig  S3 ). Allele-specific expression. As DE-kupl quantifies every SNV-containing k-mer, we set out to exploit this capacity to identify potential allele-specific expression events. We extracted all contigs including an SNV (either base substitution or indel) and mapping to an exon whose host gene was not measured as differentially expressed (Table  3 , line "SNV DU"). This was a less than perfect procedure, as we did not explicitly test for a switch in allelic balance among the two conditions. Yet, among the 717 contigs identified, some displayed strong apparent changes in allelic balance between the E and M conditions (eg. Fig  S4 ). The ability of DE-kupl to capture differential SNV between datasets may be particularly interesting when looking for recurrent mutations in subpopulations.
DE-kupl event detection is reproducible across independent datasets We cross-validated DE-kupl findings using two independent human RNA-seq datasets extracted from the Genotype-Tissue Expression (GTEx)  [24]  and the Human Protein Atlas (HPA)  [15]. DE contigs were first obtained by running DE-kupl on 8 colon vs 8 skin libraries from GTEx. Events were classified as above into intron retentions, lincRNAs, polyadenylation sites, repeats, splice sites and unmapped. The 100 top events from each class (50 for class 'unmapped') were extracted and their k-mer labels saved as a sequence file. We then counted the occurrence of each k-mer in colon and skin libraries from the HPA project and applied DEseq2  [19]  to evaluate the significance of the expression change between colon and skin (see Methods).
Discussion K-mer decomposition followed by filtering and differential expression analysis is a novel way of analysing RNA-seq data that is capable of detecting a wider spectrum of transcript variation than previous protocols. DE-kupl explores all k-mers in the input RNA-seq files (vs. only k-mers from annotated transcripts in recent software  [8, 9] ) which potentially entails heavy computational time and memory requirement. Using the Jellyfish k-mer indexing software and C-programming code for key table manipulation, we achieved time/memory requirements on par with popular mapping-based software for similarly sized datasets. Another key aspect of our protocol that rendered a "full k-mer" analysis tractable was applying successive filters for rare k-mers, Gencode transcripts and differential expression, which altogether resulted in a 200-fold reduction in k-mer counts.
These filters are not only useful for technical reasons (they reduce runtimes and enable to get rid of most sequence errors), but they also allow to focus on k-mers which (i) vary significantly between the conditions under study, and (ii) would not be captured by conventional reference-based protocols. Contrarily to popular RNA-seq analysis software, DE-kupl does not attempt fulllength transcript assignment or assembly but focuses instead on local transcript variations. Indeed, we do not consider full-length transcript analysis to be realistic when screening for unspecified RNA variation, since the combinatorial nature of genomic, transcriptomic and post-transcriptomic events would require an indefinitely expanding transcript catalog. In some way DE-kupl is closer in spirit to methods analyzing local RNA-seq coverage such as RNAprof  [25]  and DERfinder  [26] , with the notable exception that DE-kupl does not involve mapping and thus avoids mapping-related pitfalls while considerably widening the range of detectable events.
Another important benefit of the k-mer strategy is that k-mers representing events of interest can be used to efficiently assess the occurence of similar events in the huge public compendium of RNA-seq data. We showed that DE-kupl is able to detect a wide range of differential transcription and RNA processing events. Although specialized software may perform better at assessing specific event classes such as differential splicing, to our knowledge no software provides such an extensive screen. As differential RNA-seq analysis is often conducted with an exploratory spirit, we argue that it is preferable to cast a wide net with no preconception on target events, using DE-kupl along with a conventional gene-by-gene differential expression analysis. Note that DE-kupl might also be an interesting option for exploring other types of NGS data such as small-RNA-seq, ChIP-seq or CLIP-seq, with simple adjustments of k-mer size and event annotation rules.
In this proof of concept study, we focused on RNA-seq libraries from a cell line, where no genetic polymorphism was expected among samples. The next step will be application to libraries from multiple individual organisms. Although k-mer diversity will be higher in such datasets, preliminary tests with RNA-seq data from 60 human tumors were completed successfully on a single computer server (data not shown). Analysis of patient samples open exciting perspectives. For instance, the ability of DE-kupl to simultaneously detect genetic variation and RNA expression/processing events may serve as a basis for studying genotype-phenotype relations. Analysis of patient RNA-seq data may also reveal event classes not explored in this work, such as fusion transcripts and circular RNAs.
Methods Characterization of k-mer diversity in human RNA-seq libraries RNA-seq data for bone marrow, skin and colon were retrieved from the Human Protein Atlas project (10. 1126/science. 1260419, E-MTAB-2836). A total of 18 datasets from different individuals, corresponding to 6 replicates per tissue, were downloaded from EBI/ENA (bone marrow: ERR315469, ERR315425, ERR315486, ERR315396, ERR315404, ERR315406, colon: ERR315348, ERR315403, ERR315357, ERR315484, ERR315400, ERR315462, skin: ERR315401, ERR315464, ERR315460, ERR315372, ERR315376, ERR315339). The reference GRCh38 genome and Ensembl 86 transcripts were downloaded from Ensembl. First we counted k-mers in each RNA-Seq and reference sequence set using Jellyfish (2. 2. 0) count, with options k = 32 and -C (canonical k-mers). The k-mer list for each tissue (Fig  1A  and B ) was produced by merging counts for all 6 samples and conserving only those found in all replicates.
For mapping statistics (Fig 1B3 ), we extracted k-mers specific of each tissue and mapped them to the Ensembl 86 transcript reference using Bowtie (version 1. 1. 2). Unmapped k-mers were mapped a second time with Bowtie to the GRCh38 genome reference. Reads with 3 or more mismatches are not mapped by Bowtie and, therefore, are considered as unmapped. The intersection of k-mers between RNA-Seq and WGS data (Fig 1C ), is based on the transcriptome and genome of lymphoblastoid cell lines  [16]. K-mers were counted in these libraries with the same procedure as above. In order to reduce noise from sequencing errors, k-mers with only one occurrence were filtered out.
K-mer counting Raw sequences (FASTQ files) are first processed with the jellyfish count command of the Jellyfish software, which produces one index (a disk representation of the Jellyfish hash-table) for each sequence library. For stranded RNA-seq libraries, reads in reverse direction relative to the transcript are reverse-complemented, ensuring proper orientation of k-mers. At this point, for each library, only k-mers having at least 2 occurrences are recorded (user-defined parameter). Once a Jellyfish index is built, we use the jellyfish dump command to output the raw-counts in a two column text file, which contains at each line a k-mer and its number of occurrences. Raw counts are then sorted alphabetically by k-mer sequence with the Unix sort command.