dataesr/scientific-paragraphs-categorization huggingface dataset:
After 15 min incubation at room temperature, the column was washed with RA2 and RA3 buffers. RNA was eluted in 60 μl RNase-free water and stored in sterile microtubes at -80 °C. Quantity and quality of extracted RNA were assessed with RNA-specific fluorimetric quantitation on a Qubit 2. 0 Fluorometer using Qubit RNA HS Assay. The qualities of total RNA were checked by capillary electrophoresis on an Agilent Bioanalyzer, using the RNA 6,000 Pico LabChip kit (Agilent Technologies, Santa Clara, CA). Finally, the RNA extraction procedure integrated an in-column DNase treatment but, based on previous experience with this method, this step was sometimes only partially effective and did not always preclude the presence of trace DNA in final RNA samples. DNA removal in RNA samples is essential to prevent the incorporation of any genomic material in the RNA-Seq library and consequently the misinterpretation of RNA-Seq data analyses.
[3] 18S and 16S rRNA genes amplicon generation for eukaryotic and prokaryotic metabarcoding To address general questions of eukaryotic biodiversity over extensive taxonomic and ecological scales, the hypervariable loop V9 of the 18S rRNA gene was targeted for amplicon generation using DNA extracted from eukaryote-enriched fractions (0. 8-5 μm or 0. 8-3 μm, 5-20 μm or 3-20 μm, 20-180 μm and 180-2,000 μm) as template. For unravelling prokaryotic biodiversity, V4 and V5 hypervariable loops of 16S rRNA genes were co-amplified from the same DNA templates used for 18S barcoding and from DNA obtained from prokaryote-enriched fractions (0. 2-1. 6 μm and 0. 2-3 μm). Both these barcodes present a combination of advantages: (i) they are universally conserved in length and simple in secondary structure, thus allowing relatively unbiased PCR amplification across eukaryotic and prokaryotic lineages followed by Illumina sequencing, (ii) they include both stable and highly variable nucleotide positions over evolutionary time frames, allowing discrimination of taxa over a significant phylogenetic depth, (iii) they are extensively represented in public reference databases across the eukaryotic and prokaryotic tree of life, allowing taxonomic assignment amongst all known lineages.
In Metadata Record, an example of datasets produced by this strategy and available at ENA can be found. [4] Sequencing library preparation All library preparations were performed at Genoscope. 4. 1. Metagenomic library preparation from size fractionated filters DNA (Method ID: MetaG) When the Tara Oceans project started in 2009, Illumina offered a high throughput system that could enable to gain biological insights for complex samples. The counterpart was to obtain maximum read lengths of 100 bp. As short read lengths may be challenging for de novo assemblies, library preparation protocols for complex metagenomics samples were improved in order to generate much longer reads by overlapping and merging read pairs before assembly. For this purpose, a size selection step was added at the end of library preparation obtaining narrowly sized libraries around 300 bp.
This sizing step allowed isolating the prokaryotic 16S amplicon from non-specific amplification products. The library was finally purified using the Nucleospin Extract II DNA purification kit. [5] Sequencing and data quality control 5. 1. Sequencing library quality control All libraries were quantified first by Qubit dsDNA HS Assay measurement and then by qPCR with the KAPA Library Quantification Kit for Illumina Libraries (Kapa Biosystems) on an MXPro instrument (Agilent Technologies). Library profiles were assessed using the DNA High Sensitivity LabChip kit on an Agilent Bioanalyzer. Later on, the quality control step was implemented with quantification by PicoGreen method on 96-well plates and high throughput microfluidic capillary electrophoresis system for library profile analysis (LabChip GX, Perkin Elmer, Waltham, MA).
Sequencing procedures Libraries concentrations were normalized to 10 nM by addition of Tris-Cl 10 mM, pH 8. 5 and then applied to cluster generation according to the Illumina Cbot User Guide (Part # 15006165). Libraries were sequenced on Genome Analyzer IIx, HiSeq2000 or HiSeq2500 instruments (Illumina) in a pairedend mode. Read lengths were chosen in order to produce data fitting with bioinformatics analyses needs (Table  2 ). Metabarcoding and metatranscriptomic libraries were characterized by low diversity sequences at the beginning of the reads related respectively to the presence of primer sequence used to amplify 18S and 16S tags and low complexity polynucleotides added during cDNA synthesis. Low-diversity libraries can interfere in correct cluster identification, resulting in drastic loss of data output.
Data quality control and filtering A first step in data quality control process was the primary analysis performed during the sequencing run by Illumina Real Time Analysis (RTA) software (Code availability 1). This tool analyses images and clusters intensities and filters them to remove low quality data. Furthermore, it performs basecalling and calculates Phred quality score (Q score), which indicates the probability that a given base is called incorrectly. Q score is the most common metric used to assess the accuracy of the sequencing experiment (http://www. illumina. com/documents/products/technotes/technote_Q-Scores. pdf). After conversion of raw BCL files generated by RTA to fastq demultiplexed data by Illumina bcl2fastq Conversion software (Code availability 2), in-house filtering and quality control treatments developed in Genoscope were applied to reads that passed the Illumina quality filters (named raw reads).
The parameters of these controls are indicated in Fig. 2. This processing allows obtaining high quality data and improves subsequent analyses. Filtering steps were applied on whole raw reads as follows: -The sequences of the Illumina adapters and primers used during library construction were removed from the whole reads. Low quality nucleotides with quality valueo20 were removed from both ends. The longest sequence without adapters and low quality bases was kept. Sequences between the second unknown nucleotide (N) and the end of the read were also trimmed. Reads shorter than 30 nucleotides after trimming were discarded. These trimming steps were achieved using fastx_clean (Code availability 3), an internal software based on the FASTX library (Code availability 4). -The reads and their mates that mapped onto run quality control sequences (Enterobacteria phage PhiX174 genome, Data Citation 1) were removed using SOAP aligner  47.
-A specific filter aiming to remove ribosomal reads was applied to data generated from metatranscriptomic libraries sequencing: briefly, the reads and their mates that mapped onto a ribosomal sequences database were filtered using SortMeRNA v 1. 0 (ref. 48), a biological sequence analysis tool for filtering, mapping and OTU-picking NGS reads. It contains different rRNA databases and we used it to split the data into two files: rRNA reads in a file (ribo_clean) and other reads in another file (noribo_clean). Data quality control was performed on random subsets of 20,000 reads before (raw reads) and/or after filtering steps (clean reads) as follows: -Duplicated sequences rates were estimated from single and paired sequences on raw reads, using fastx_estimate_duplicate (Code availability 5), an internal software based on the FASTX library.
-Read size, quality values, N positions, base composition were calculated and known adapters sequences were detected before and after filtering the reads. -Taxonomic assignation was performed by aligning with Mega BLAST (Blast 2. 2. 15 suite)  49  the subset of 20,000 reads against the nt database (http://www. ncbi. nlm. nih. gov/nucleotide), and using Megan software (version 3. 9)  50. -The merging step was done with fastx_mergepairs (Code availability 6), an internal software based on the fastx library. The first 36 nucleotides of read2 were extracted and alignment performed between that seed and read1. Merging was launched if the alignment was at least of 15 nucleotides, with less than 4 mismatches and an identity percent of at least 90%. For each overlapping position, the nucleotide of higher quality was retained.
Data Records This data descriptor provides an opportunity to present collections of different datasets generated from sequencing analysis of samples collected during Tara Oceans expedition. Fastq files produced from sequencing experiments are available in the global repository for public nucleotide sequence data, the International Nucleotide Sequence Database Collaboration (INSDC, http://www. insdc. org/about) under the umbrella project permanent identifier PRJEB402 'Tara-oceans samples barcoding and shotgun sequencing' (Data Citation 2). Nucleotide sequence information has been deposited via the European gateway to the INSDC, the European Nucleotide Archive (ENA, http://www. ebi. ac. uk/ena) at the EMBL European Bioinformatics Institute (EMBL-EBI). Nucleotide sequence data of each sequencing strategy applied to the size-fractioned Tara Oceans plankton communities are registered in a separate component project and linked to the PRJEB402 umbrella project.
For instance, the metatranscriptome sequencing of samples from the size fraction of protists is registered under the component project with the identifier PRJEB6609 and available at http://www. ebi. ac. uk/ena/data/view/PRJEB6609. Each generated nucleotide sequence file is associated with a sample record containing extremely rich information on the environment of the corresponding sequenced Tara Oceans sample. The sample contextual data available with nucleotide sequences map to the environmental and biogeochemical measurements for each Tara Oceans sample available in the Sample Registry at PANGAEA (Data Citation 3). A list of available FASTQ files with repository information is presented in the associated Metadata Record and is also available at PANGAEA (Data Citation 4). Most importantly, this metadata document allows to link each FASTQ file to the experimental protocols used for their generation.
In order to help scientists to correctly manipulate these data, it is important to underline that in many cases, two (or, sporadically, more) FASTQ files were generated by repeated sequencing of the same library. Consequently, FASTQ files sharing the same library name should be pooled for bioinformatics analyses. This high contextualization of all Tara Oceans sequence data makes the whole dataset a unique and valuable tool to marine ecosystem biologists and can serve as an example to other large-scale data generating projects.
Technical Validation Sample and experiments information management All samples received by Genoscope and all experiments performed from nucleic acid extractions to sequences generation were tracked by an in-house Laboratory Information Management System (LIMS). This LIMS is designed to accumulate information on each sample at each step of the processes. This software has been essential for internal follow-up at any stage of the processing of such a huge amount of samples. With this approach, all collecting data (station, depth, porosity), experiment data (protocol and quality control results) and bioinformatics quality control analyses (duplicates, contamination, taxonomy, mapping, merging) follow each sample throughout the experiment chain until sequencing data analysis. All these properties can be used to search and display sample data in reports during all the process.
Total Eukaryotic RNA Assay was selected for electropherogram internal analysis for RNA extracted from protist-and metazoan-enriched filters whereas Total Prokaryotic RNA Assay was applied to prokaryote-enriched filters. These software allow generation of a RNA Integrity Number (RIN) calculated by comparing rRNA peaks with a specific database (eukaryotic or prokaryotic or plants) and usually used as a score of RNA quality. In many Tara Oceans RNA samples, particularly from 0. 8-5 μm, 0. 22-1. 6 μm and 0. 22-3 μm filters, eukaryotic and prokaryotic species were co-extracted, generating atypical rRNA peaks profiles. For this reason, the RIN was not accurate or even not computable and did not reflect the quality of the preparations. However, as for DNA preparations, RNA quality was sometimes poor as most Agilent profiles showed rRNA peaks but also variable amounts of small size RNA, indicating partial degradation.
Quality control: Data validation For the Tara Oceans project, specific report configurations were developed within internal LIMS to display, compare and analyse hundreds of samples. The quality of each dataset was assessed using the interfaces depicted in Figs 5 and 6 which provided a quick insight into the dataset quality allowing smart check of important statistics as the number of passed filter reads and duplication rate; % rRNA reads for metatranscriptomic datasets; and % merged reads, their median and average size for metagenomics ones. Furthermore, for each sample, plots and graphics were generated and allowed to easily visualize base quality (Q score) and nucleotide distribution before and after the filtering treatments described in paragraph 5. 3, as well as taxonomical assignments (Fig.
6 ). Among all the sequences quality control statistics, the following parameters were considered crucial for identification of low quality sequences and drove decision about passing quality control: -Base calling accuracy measured by Phred quality score (Q score): for each sample, this metric was illustrated by the calculation of Q score mean and % of bases with Q ≥ 30 (Fig. 5a ) as well as a quality score plot (Fig. 6a ). A Q score of 30 means the probability of an incorrect base call 1 in 1,000 times, in other words that the base call accuracy is 99. 9%. Generally, datasets were valid when Q score mean was >30 and % of bases with Q ≥ 30 was at least 80%. However, this last criterion was not applied to metatranscriptomics and amplicon libraries as the particular library construction and the low base diversity, especially in the case of amplicons, had a negative impact on the Q score by decreasing it significantly.
-Taxonomic assignation (Fig. 6d, f ): the majority of the reads were classified under the 'no hits' or 'not assigned' item, as expected from the origin of the samples. Otherwise, the sequences were assigned to known marine species. The presence of other species not expected to live in ocean environment was considered as contaminating the sample and a threshold of 2% per species was arbitrarily defined to invalidate the dataset. However, this kind of contamination was rare and in most cases attributed to Homo sapiens DNA. Whereas these criteria were applied to all samples, others were defined only for metatranscriptomics samples issued from protists/metazoan-enriched filters. In particular: -High duplicate rates (calculated on raw paired end reads) were considered as characteristic of low complexity samples.
Datasets containing>20% duplicates were not further processed (Fig. 5b ). -Taxonomic assignations attributed to bacteria were not expected in these samples, produced by poly (A) + RNA selection, and considered as a background signal. An arbitrary threshold defined at 5% was applied for passing quality control (Figs  5b  and 6d ). -Datasets containing more than 5% reads assigned to the Fungi kingdom were further inspected. Most of them were classified as filamentous ascomycete fungi, a group including both marine and terrestrial species. Verification about the habitat of the suspected contaminant was made before taking decision of discarding the dataset (Figs 5b and 6d).
Again, the overrepresentation of SMARTer adapter is easily visualised on the left panel (red bar) and it disappears after the trimming process (right panel). (d) Report of taxonomic assignation by organism (left), by division (middle) and by keyword (right). Bacteria and fungi %o5% are highlighted in green to facilitate manual validation of the dataset. (e) Report of rRNA sequences detection and trimming with detail of % of different rRNA species. (f) Krona chart of the same taxonomic assignment reported in (d). (g) Distribution of the length of the reads obtained after merging of paired reads generated by sequencing of a metagenomic library.
Acknowledgements We thank the commitment of the following people and sponsors:  CNRS  (in particular Groupement de Recherche  GDR3280 ),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA , the  French Government 'Investissements d'Avenir' programmes OCEANOMICS  ( ANR-11-BTBR-0008 ) and  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  Agence Nationale de la Recherche ,  European Union   FP7  ( MicroB3/No. 287589 )and the  U. S. National Science Foundation  awards  DEB-1031049 ,  OCE--0623288 ,  OCE-821374  and  OCE-1019242  (to M. E. S. and R. S. ) and  OCE-1335810  (to R. S. ). Additional funding was provided by  Spanish Ministry of Science and Innovation  grant  CGL2011-26848 /BOS MicroOcean PANGENOMICS and by  Japan Society for the Promotion of Science (JSPS)/KAKENHI  (grant numbers  26430184 ,  16H06429 ,  16K21723  and  16H06437 ).
Code availability 1. Real Time Analysis software: http://support. illumina. com/sequencing/sequencing_software/real-time_-analysis_rta/downloads. html 2. Bcl2fastq Conversion: http://support. illumina. com/sequencing/sequencing_software/bcl2fastq-conversion-software. html 3. Fastx_clean software, http://www. genoscope. cns. fr/fastxtend 4. FASTX-Toolkit, http://hannonlab. cshl. edu/fastx_toolkit/index. html 5. fastx_estimate_duplicate software, http://www. genoscope. cns. fr/fastxtend 6. fastx_mergepairs software, http://www. genoscope. cns. fr/fastxtend.
Data Citations 1. GenBank NC_001422. 1 (2015). 2. European Nucleotide Archive PRJEB402 (2012). 3. Tara Oceans Consortium, C. & Tara Oceans Expedition, P. PANGAEA http://dx. doi. org/10. 1594/PANGAEA. 859953 (2015). 4. Alberti, A. , Pesant, S. & Tara Oceans Consortium, C. & Tara Oceans Expedition, P. PANGAEA https://dx. doi. org/10. 1594/ PANGAEA. 875581 (2017).
Using PROV to Model Context We have chosen the Provenance data model (PROV) and especially the PROV Ontology (PROV-O) to represent the activities, the resources they use and produce, and their actors. PROV-O is an owl ontology providing a simple data model proposed by the W3C for which provenance is defined as "a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing" (W3C, 2013). One of the main ideas from which PROV has emerged is that provenance of information is crucial in deciding whether information is to be trusted and how to give credit to its originators when reusing it. Such intention clearly matches our needs focusing on the provenance of the tags and the shared experience they are related to.
We introduce the grab 'n' drop toolglass, an extension of the toolglass bi-manual interaction technique. It enables users to create and configure their own toolglasses from existing user interfaces that were not designed for toolglasses. Users compose their own toolglass interactions at runtime from an application's user interface elements, bringing interaction closer to the objects of interest in a workspace. Through a proof-of-concept implementation for Mac OS X, we show how grab 'n' drop capabilities could be added to existing applications at the toolkit level, without modifying application source code or UI design. Finally, we evaluate the power and flexibility of this approach by applying it to a variety of applications. We further identify limitations and risks associated with this approach and propose changes to existing toolkits to foster such user-reconfigurable interaction.
Related work The grab 'n' drop toolglass builds on toolglasses, certainly, but also on work in runtime, third-party program modification, on end-user customization, and on user-interface toolkits and interactions. Toolglasses and magic lenses  [4]  were first proposed by Bier et al. in 1993 as a general-purpose interaction technique, a new style of widget that programmers could add to their toolbox. Various extensions have been proposed, including toolglasses for debugging  [13]  and visualization  [11]. Moreover, they act as a strong complement to other tools, such as floating palettes and marking menus  [20]. Perhaps the closest in spirit to our proposed technique is the style toolglass in Beaudouin-Lafon & Mackay's CPN2000  [3]. Initially, empty items in the toolglass act as a style picker, extracting attributes such as color and thickness.
End-user customization Non-programmer end-users have a long history of customizing their software to suit their own particular needs  [19]. With sufficient scaffolding and motivation, they can even go as far as to program  [9, 22] , and even construct sharing communities  [12, 17] , with different users assuming different roles to encourage and foster such customization  [12]. Our work draws inspiration from these approaches. In this context, our goal is to enable users to compose new interactions from existing program functionality without requiring them to program or manipulate complex configuration dialogs. As such, we draw inspiration from programming by demonstration  [7] : a user simply clicks through interface elements to construct a personal toolglass. Grab 'n' Drop toolglass do not, however, attempt to infer intent from user's actions.
Lab notebook Denise is documenting a lab procedure in an email. In the control software for a piece of lab equipment, she creates a new toolglass for the procedure and clicks through each of the relevant controls to extract its associated action. She then switches to her email message and command-clicks the controls in the toolglass to drop their stored screenshot as a rich-text attachment, integrating screenshots of the configured controls into the text of the procedure. Some time later, Eric reads the procedure and command-clicks through an empty bucket onto the image of one of the controls embedded in the document in order to grab it into the toolglass. He then command-clicks through the bucket onto the relevant control to apply the state described in the image to the underlying control.
Live screenshots The typical use of grab 'n' drop toolglasses is to compose a personal toolglass out of existing widget components, effectively treating the program's interface as malleable and re-composable. We further extended the toolglass to support treating the live interface as an embeddable object in a document. As such, the user can drop the captured screenshot of the widget in a bucket into a document, as in the lab procedure scenario described above. Indeed this scenario itself comes from Klokmose & Zander's work with scientists on laboratory notebooks  [16]. When the user command-drops a bucket, the toolglass passes the click through to the underlying window, then inserts a screenshot of the widget as if pasted from the clipboard. In the meta-data of the image, the toolglass writes the same information that would be serialized for subsequent program invocations (described in more detail in the following section).
As such, the dropped image contains both a human-readable representation of the associated widget, but also a machine-readable description sufficient to re-connect the image to the associated widget, similarly to how Kato et al. encode robot pose data in images embedded in source code  [15]. Because all of the machine-readable information necessary to link the image to the widget is stored in the meta data of the image, any existing document or image editor that preserves the embedded meta-data can be used to edit that document. Re-establishing the link requires only that the toolglass be able to map a click to the image, which is currently implemented using the Mac OS X Cocoa rich text editing APIs.