dataesr/scientific-paragraphs-categorization huggingface dataset:
After deployment, Argo floats descend to a pre-programmed "parking" depth (generally 1000m) where they drift with the current for approximately 10 days, then ascend to the surface (taking a profile of temperature and salinity) and transmit their location by satellite to a data center. The floats then re-descend to their parking depth and repeat the cycle. With knowledge of the time between each surfacing, as well as the distance between the surfacing locations, one can estimate the parking depth velocity  [14]. In this study, we make use of the ANDRO data set (http://www. umr-lops. fr/Donnees/ANDRO), described by Ollitrau & Rannou  [14]. This dataset provides estimates of the current velocity at the float parking depth between 2005 and 2011 and covers the entire Southern Ocean north of about 65 • S, although, as can be seen in Fig  1(b ), the data are not evenly spatially distributed.
There are 122,174 independent data records in the dataset and errors due to the delay between the float surfacing and the satellite location fix and vertical shear in the water column are estimated to be small. In this study, we restrict our attention to the Southern Ocean between 65 • S and 35 • S. We also use only floats with parking depths within 50m of 1000m, as there are sufficient floats at this depth to enable broad coverage of the Southern Ocean. In contrast, the number of floats at parking depths different from 1000m is much more limited and they are not able to provide broad geographic sampling  [14].
The R 2 of each velocity component is above 0. 95, and the speed RMSE is 2. 8cm. s -  , more than a factor of 2 smaller than those obtained by Meijers et al. [6]  and a factor of 3 smaller than those obtained from dynamical type methods such as Isern-Fontenet et al  [7]. To ensure that our method is not subject to overfitting, we also validate against the training data, obtaining a speed RMSE of 2. 6cm. s -. This value is sufficiently close to the RMSE obtained from the validation data that we can rule out over-fitting. We note that using the SOM methodology on its own, without the correction described by Eqn. 1, gives inferior results, with RMSEs of ∼7. 5cm. s -  and R 2 =∼0. 6. We compute the spatial distribution of the errors in both the current speed and direction, quantified by the bearing angle θ = tan -1 (v/u), by determining the error at the location of each deep velocity observation in the validation dataset, then mapping the results to a regular latitude/longitude grid with 0.
B. Reconstruction of Deep Southern Ocean Currents We now use our method to reconstruct maps of deep currents over the entirety of the Southern Ocean basin. To do this, we apply our method to each of the daily output maps in the AVISO and OISST databases, between 2005 and 2011. We obtain 5 years of daily current velocities (1826 snapshots) at 1000m on a regular latitude/longitude grid with 1/4 • grid spacing. Grid points with fewer than 10 deep velocity observations within 150km (typically south of 65 • S) are masked. Fig. 5  shows the reconstructed time mean current speed for the year 2009. Our reconstructed currents display realistic behavior such as self-organisation into complicated smallscale (O(10-20km)) "jets" (e. g. south of Africa between 20 • E and 50 • E), steering by subsurface topography (e.
IV. DISCUSSION AND CONCLUSIONS In this letter, we have used a machine-learning technique to reconstruct the velocity of ocean currents at 1000m depth from satellite observations. Our results yield errors that are 2 to 3 times smaller than competing methods. We are able to   Despite the promising results, our methodology has several shortcomings. Most notably, to train the SOM we require velocity information at depth. While we have been able to exploit the near global coverage provided by Argo floats at 1000m, velocity data are more limited at other depths  [14] , which reduces our ability to apply this method more generally. The satGEM dataset of Meijers et al. [6]  and dynamical methods  [7]  are not limited by the availability of deep velocity data and can provide reconstructions at any depth.
, 2015)  or using a protein reference set from a phylogenetically closely related organism. Last, some software packages are also rating the contig set or the individual contigs using the above-mentioned criteria  (Honaas et al. , 2016)  or some other for example only related to the way reads map back to the contigs  (Smith-Unna et al. , 2016; Li et al. , 2014; Davidson & Oshlack, 2014). We have built a de novo RNA-Seq Assembly Pipeline (DRAP) in order to correct the following assembly problems: multiple copies of complete or partial transcripts, chimeras, lowly expressed intergenic transcription, insertion and deletion generated by the assemblers and polyA tails. The pipeline implementation is presented in the next section. The ''results and discussion'' section compares raw and DRAP assembly metrics for seven different datasets.
IMPLEMENTATION DRAP is written in Perl, Python, and shell. The software is a set of three command-line tools respectively called runDrap, runMeta and runAssessment. runDrap performs the assembly including compaction and correction. It produces a contig set but also a HTML log report presenting different assembly metrics. runAssessment compares different contig sets and gathers the results in a global report. runMeta merges and compacts different contigs sets and should be used for very large datasets for which memory or CPU requirements do not enable a unique global assembly or for highly complex datasets. The modules chained by each tool are presented in a graphical manner in Figs. 1, 2 and 3. Details on the compaction, correction and quality assessment steps of the tools are described hereafter.
This workflow is used to evaluate quality for one assembly or for compare several assemblies produced from the same dataset. Inputs are the assembly/ies, R1 and eventually R2, and a reference protein set. and depth. The Oasesv2. 0. 4BestTransChooser. py script sub-selects the longest and most covered contig of a locus. The second compaction method removes contigs included in longer ones. CD-HIT-EST  (Fu et al. , 2012)  orders the contigs by length and removes all the included ones given identity and coverage thresholds. The third method elongates the contigs through a new assembly step. TGICL  (Pertea et al. , 2003)  performs this assembly in DRAP. The last approach filters contigs using their length or the length of their longest ORF if users are only interested in coding transcripts, and using read coverage according to the idea that lowly covered contigs often correspond to noise.
A last optional filter selects contigs using their TransRate quality score when above the calculated threshold (-optimize parameter). By default, runDrap produces eight contigs sets, four include only protein coding transcripts and four others contain all transcripts. Each group comprises a contig set filtered for low coverage with respectively 1, 3, 5 and 10 fragments per kilobase per million (FPKM) thresholds. Compaction favors assemblies having contigs with multiple ORFs. Because a unique ORF is expected for contig annotation, DRAP splits multi-transcript chimera in mono-ORF contigs. runMeta also performs a three step compaction of the contigs. The first is based on the contig nucleotide content and uses CD-HIT-EST. The second run CD-HIT on the protein translation of the longest ORF found by EMBOSS getorf.
Contig set corrections Contig correction splits chimeras, removes duplicated parts, removes insertions, deletion and polyA/T tails. DRAP corrects contigs in three ways. It first searches self-chimera and removes them by splitting contigs in parts or removing duplicated chimeric elements. An in house script aligns contigs on themselves using bl2seq and keeps only matches having an identity greater or equal to 96%. A contig is defined as a putative chimera if (i) the longest self-match covers at least 60% of the contig length or (ii) the sum of partial non-overlapping self-matches covers at least 80% of its length. In the first case, the putative chimera is split at the start position of the repeated block. In the second case, the contig is only a repetition of a short single block and is therefore discarded.
For the second correction step, DRAP searches substitutions, insertions and deletions in the read realignment file. When found it corrects the consensus according to the most represented allele at a given position. Low read coverage alignment areas are usually not very informative therefore only positions having a minimum depth of 10 reads are corrected. The manual assessment made on DRAP assemblies has shown that a second path of this algorithm improves consensus correction. Part of the reads change alignment location after the first correction. runDrap, consequently, runs this step twice. The last correction script eases the publication of the contig set in TSA (https: //www. ncbi. nlm. nih. gov/genbank/tsa): NCBI transcript sequence assembly archive. TSA stores the de novo assembled contig sets of over 1300 projects.
In order to improve the data quality, it performs several tests before accepting a new submission. These tests search for different elements such as sequencing adapters or vectors, polyA or polyT and stretches of unknown nucleotides (N). The thresholds used by TSA are presented at https://www. ncbi. nlm. nih. gov/genbank/tsaguide. DRAP performs the same searches on the contig set and corrects the contigs when needed.
Quality assessment All three workflows create an HTML report. The report is a template including HighCharts (http://www. highcharts. com) graphics and tables using JSON files as database. These files are generated by the different processing steps. The report can therefore also be used to monitor processing progression. Each graphic included in the report can be downloaded in PNG, GIF, PDF or SVG. Some of the graphics can be zoomed in by mouse selecting the area to be enlarged. The report tables can be sorted by clicking on the column headers and exported in CSV format. For runDrap and runMeta, the reports present results of a single contig file. runAssessment processes one or several contig files and one or several read files. It calculates classical contig metrics, checks for chimeras, searches alignment discrepancies, produces read and fragment alignment rates and assess completeness using an external global reference running BUSCO.
Parallel processing and flow control DRAP runs on Unix machines or clusters. Different steps of the assembly or assessment process are run in parallel mode, if the needed computer infrastructure is available. All modules have been implemented to take advantage of an SGE compliant HPC environment. They can be adapted to other schedulers through configuration file modification. DRAP first creates a set of directories and shell command files and then launches these files in the predefined order. The '-write' command line parameter forces DRAP to stop after the first step. At this stage, the user can modify the command files for example to set parameters which are not directly accessible from runDRAP, runMeta or runAssessment and then launch the process with the '-run' command line option. DRAP checks execution outputs at each processing step.
A transcript is linked to a contig if its best blat hit has over 90% query identity and 90% query coverage. The test has been performed on the Dr and the Ds datasets assembled with Oases and Trinity. The number of alternative spliced isoforms decreases more, with   or without DRAP, in the Oases than in the Trinity assemblies (Table  5 ). This reduction is of 69% and 23% in the real dataset (Dr) and 83% and 18% in the simulated dataset for Oases and Trinity respectively. However, the spliced forms reduction does not impact the gene representation in the compacted sets (Table  5 ). Remarkably, the gene representation is increased for the real dataset when processed with DRAP Oases. This results from the different merging strategies used by Oases and DRAP Oases. Using TGICL, DRAP is able, in some cases, to correctly merge gene parts which have been generated by the Oases multi-k assemblies and this more efficiently than the build-in Oases merge procedure.
Contig set corrections DRAP corrects contigs in two ways: removing self-chimera and rectifying consensus substitutions, insertions and deletions when the consensus does not represent the major allele at the position in the read re-alignment file. 2 colum 12 ). (B), (C) and (D) present the ratio of the error rates respectively for substitution, insertions and deletions between raw and DRAP assemblies for each dataset (data from Table  S2 ). contig sets at rate ranging respectively from 0. 11 to 1. 39 and from 0. 09 to 0. 56%. In DRAP, the corresponding figures drop to 0. 01 to 0. 16 and 0. 00 to 0. 01%. Concerning consensus correction only five datasets can be taken into account i. e. , At, Bt, Dm, Ds and Hs. Dr Oases assembly generates such a large number of contigs and total length that it decreases significantly the average coverage and therefore limits the number of positions for which the correction can be made.
As shown in Fig. 5  and Table  S2 , the Dr dataset is an outlier concerning this criteria. Regarding the five other datasets raw versus DRAP correction rates range from 1. 7 to 18. 6 for insertions, 3. 1 to 27. 1 for deletions and 2. 7 to 14. 1 for substitutions. DRAP correction steps lowers significantly the number of positions for which the consensus does not correspond to the major allele found in the alignment. In order to check the positive impact of the correction step, the Danio rerio reference proteome has been aligned to the simulated dataset (Ds) contigs before and after correction. 94. 5% of DRAP Oases contigs and 86. 2% of DRAP Trinity contigs which have been corrected, have improved alignment scores (Data S1 section ''Contig set correction step assessment'').
Assembly quality assessment The two previous parts have shown the beneficial impacts of DRAP on the assembly compactness and error rates but this should not impair quality metrics such as read and read pairs alignment rates, number of ORFs, complete ORFs found in the contigs, number of proteins of the known proteome mapped on the contigs or TransRate marks. Read and read pair alignment rates differences between raw and DRAP assemblies are usually very low, between 1 and 2% and can sometimes be in favor of DRAP (Fig. 6 ). In our test sets, the difference is significant (7. 5%) for Dm when comparing Trinity to DRAP Trinity. This comes from the removal by DRAP of a highly expressed transcript (Ensembl: FBtr0100888 mitochondrial large ribosomal RNA) because that does not fulfill the criteria of having at least one 200 base pairs long ORF despite having over 11M reads aligned on the corresponding contig in the Trinity assembly.
DRAP Oases assembly was not impacted because it builds a longer contig for this transcript with a long enough ORF to be selected in the additional part. The reference proteome has been aligned on the contigs and matches with over 80% identity and 80% protein coverage have been counted (Fig. 7 ). These figures give a good overview of the amount of well-reconstructed proteins in the contig sets. For all datasets except one (At) the number of proteins are very close between raw and DRAP results. For this At dataset the difference is of 12. 2% for Oases and 13. 2% for Trinity. This is due to the FPKM filtering step performed by DRAP and the expression profile of this dataset that mixes different tissues (root, shoot and flower) and conditions (full nutrition and starvation). Contigs corresponding to low expression in one condition do not have sufficient overall expression to pass DRAP expression filter threshold and are therefore eliminated from the final set.
Mixed libraries can benefit from the meta-assembly approach presented in the next section. TransRate global scores (Fig. 8 ) are much higher for DRAP assemblies compared to raw ones. This comes from the compaction performed by DRAP and the limited impact it has on the read alignment rate. DRAP has limited negative effect on the assembly quality metrics, and sometimes even improves some of them. Some cases in which multiple libraries are mixed with very distinct conditions can affect the results and it is good practice to systematically compare raw and DRAP assemblies. It is also to be noticed that Oases multi-k strategy outperforms Trinity for all datasets regarding the number of well-reconstructed proteins.
continued) The theta0 value was calculated with the rsem-calculate-expression program on read files from the Danio rerio pineal gland sample (SRR1048059). Table1also presents for each dataset: the number, length, type (paired or not) and strandedness of the reads, the public accession number, the tissue and experimental condition of origin. The results presented hereafter compare the metrics collected from Trinity, Oases, DRAP Trinity and DRAP Oases assemblies of the six first datasets. The multi sample dataset has been used to compare a strategy in which all reads of the different samples are gathered and processed as one dataset (pooled) to a strategy in which the assemblies are performed by sample and the resulting contigs joined afterwards (meta-assembly). The same assembly pipeline has been used in both strategies, except the contig set merging step, which is specific to the meta-assembly strategy.
Funding This work was supported by funds from the  ANR  (http://www. agence-nationalerecherche. fr/) in the frame of the  PhyloFish  ( ANR-10-GENM-017 ) and the  France Génomique  ( ANR10-INBS-09-08 ) projects. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Summary Table2and Table3present the metrics collected for the six first datasets. Table2provides metrics related to compaction and correction as Table3includes validation metrics and Table 4 collects all three metric types for pooled versus meta-assembly strategies. Name Species Layout Library Protocol Paired Stranded Length Nb R1 SRA ID Tissue Condition (nt) 7,420,748 SRR1048062 Pineal gland Dark 9,737,614 SRR1048063 Pineal gland Dark Ds Danio rerio Yes No 100 30,000,000 Simulated - - Hs Homo sapiens No No 25-50 15,885,224 SRR2569874 TK6 cells pretreated with the protein kinase C activating tumor 15,133,619 SRR2569875 TK6 cells pretreated with the protein kinase C activating tumor 19,312,543 SRR2569877 TK6 cells pretreated with the protein kinase C activating tumor 21,956,840 SRR2569878 TK6 cells pretreated with the protein kinase C activating tumor Bos taurus: Bt, Drosophila melanogaster: Dm, Danio rerio: Dr and Homo sapiens: Hs), one set comprising a large number of diverse samples (Danio rerio multi samples: Dd) and one simulated dataset (Danio rerio simulated: Ds).
The figure shows the number of proteins which have been aligned on the contig sets with more than 80% identity and 80% coverage for each assembler and dataset. NbBproteins 10k 15k 20k 25k 23,457 20,895 20,290 17,916 10,560 10,456 10,313 10,144 6,873 6,731 6,494 6,358 15,186 14,901 10,734 11,272 14,929 14,913 14,394 14,364 7,554 7,463 7,199 7,124 5k 0k At Bt Dm Dr Ds Hs RawBOases DRAPBOases RawBTrinity DRAPBTrinity Figure 7 Proteins realignment rates.
In this article, we describe the design and implementation of an e-internship program that BioScience Project offers high school students over the summer. Project topics are in the areas of behavioral neuroscience and brain disorders. All research, teaching, and communication is done online using open access databases and webtools, a learning management system, and Google apps. Students conduct all aspects of a research project from formulating a question to collecting and analyzing the data, to presenting their results in the form of a scientific poster. Results from a pilot study involving fifteen students indicate that students are capable of successfully completing such a project, and benefit both intellectually and professionally from participating in the e-internship program.
Introduction Neurogenomics is the study of the systems, networks, and gene interactions that underlie neural processes. Increased functional information from diverse sources available in open access databases, along with specific tools for analysis, enables the integration of these data to gain unique insights  (Overall et al. , 2015). BioScience Project (www. bioscienceproject. org) offers high school students the opportunity to work as summer interns on research projects in the area of behavioral neuroscience and brain disorders, which includes the analysis of gene expression data using systems and network biology methods (see  Schughart & Williams, 2017). This is a voluntary internship available to students regardless of academic performance or institution. Students participate in the program to gain hands on experience and acquire new skills.
The projects involve learning how to formulate and test hypotheses, data-mine biological and neuroscience-specific databases, statistical analysis, and data representation and visualization. Students need only a computer with an Internet connection to participate. The projects are flexible, allowing students to work from home on their own schedule. All communication is done via the Internet with an online learning management system (Moodle, https:// moodle. com/), Google apps (https://gsuite. google. com/), and video conferencing. At the end of the internship, students communicate their work in a poster, which can be used to leverage their college applications and/or detail their experience to prospective employers. Students also receive certificates of completion. Several strengths of the e-internship program worth noting are: (1) Students are highly interested in topics related to behavioral neuroscience and brain disease; (2) This is shown to be an effective model to introduce early stage students to advanced topics and research methods in neuroscience; (3) Students receive the otherwise-limited opportunity to participate in authentic research projects and work directly with professional scientists; (4) The internship program is scalable, enabling many students to participate; (5) Project results are freely accessible to the scientific community on BioScience Project's website (www.
REVISED conferencing and consist of a walk-through of each database with screen sharing, project discussion, and troubleshooting of problems. Mentoring sessions typically take place weekly and last anywhere from 30 to 90 min. Mentoring is requested and scheduled by email. All instruction and mentoring is provided by the project director, Dr. Anna Delprato. As the internship program grows, additional scientists will be recruited to assist with teaching. Students are not tested and there are no grades assigned. Teaching and communication is done through an online learning platform (Moodle; https://moodle. com/), one on one video conferencing (Skype or Google Hangouts), email, document sharing (Google Docs), and a Google group, which enables students to receive notices and communicate with one another.
Project design The protocol used for profiling gene expression data is broken down into steps so as not to overwhelm the students with too much information at once. Modules which consist of detailed instruction are built around each step of the protocol. The purpose of the module is to provide the students with a detailed reference in addition to the mentoring sessions so that they can work in the databases and with the data independently. The starting point for all projects involves the identification of brain regions associated with a behavioral process or brain disease, which is based primarily on functional magnetic resonance imaging (fMRI) data. Students find this information through an Internet search with our assistance. Gene expression patterns are then analyzed to identify those genes that are preferentially expressed in these brain areas across all donor brains.