dataesr/scientific-paragraphs-categorization huggingface dataset:
Gating of representative replicate grown in ENR (top) and percentage of GFP hi cells normalized to ENR DMSO condition of 3 biol. replicates, indicated by shape. Mean highlighted. Paired t-test (bottom). Percentage of total GFP+ cells is shown in Supplementary Figure 3d. (D) Volcano plot of mRNA sequencing of untreated vs. I-CBP112 treated organoids, 4 biol replicates per group. Selected genes are highlighted. (E) mRNA sequencing of untreated vs. I-CBP112 treated organoids. GSEA of LGR5+ stem cell signature from Muñoz et al. (2012) (GSE33949). Normalized enrichment score (NES) and false discovery rate (FDR) are indicated.
Representative gating and quantification in 3 biol. replicates, indicated by shape. Paired t-test. (E) Volcano plot of mRNA sequencing of untreated vs. MS023 treated organoids, 3 biol replicates per group. Selected genes are highlighted. (F) mRNA sequencing of untreated vs. MS023 treated organoids. GSEA for Paneth cell, goblet cell, enteroendocrine cell, and tuft cell signatures from Haber et al. (2017) (GSE92332). Normalized enrichment scores (NES) and false discovery rates (FDR) are indicated. (G) Apc-deficient adenomas treated with DMSO, MS023, or TC-E5003 for 96 h. Representative well (top, full well shown in Supplementary Figure 6j, scale bar shows 500 µm) and quantification of organoid size and mean gray value, 7 individual wells per condition (bottom). (H) Organoids treated with DMSO, MS023, or TC-E5003 for 96 h.
FUNDING This work was provided by the  Norwegian Research Council (Young Research Talent 274760 to MO and Centre of Excellence  grant  223255/F50 ) and the  Norwegian Cancer Society  ( 182767  to MO). The flow cytometry screen was carried out at  Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP) Screening Unit, Berlin, Germany  with funding from  H2020 CORBEL (PID6031  to JO). ELIXIR Norway and the  Centre for Digital Life Norway , funded by the  Norwegian Research Council  ( 270068  and  248810/O70 ) assisted with data management.
DATA AVAILABILITY STATEMENT The Imaging data from the initial screen was deposited to the Image Data Resource  (Williams et al. , 2017 ; https://idr. openmicroscopy. org) under accession number idr0092. The Ilastik projects and respective training data of the initial screen organoid segmentation were deposited to Zenodo under https:// doi. org/10. 5281/zenodo. 4311473. The qRT-PCR data from the initial screen was deposited along with processed data from follow-up experiments to BioStudies database at EMBL-EBI  (Sarkans et al. , 2018 ; https://www. ebi. ac. uk/biostudies) under accession number S-BSST447. RNA-seq data were deposited in the ArrayExpress database at EMBL-EBI  (Athar et al. , 2019;    https://www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-9290 (I-CBP112-treated samples) and E-MTAB-9291 (MS023-treated samples).
Artificial Intelligence (AI) has the promise of providing a paradigm shift in battery R&D by significantly accelerating the discovery and optimization of materials, interfaces, phenomena, and processes. However, the efficiency of any AI approach ultimately relies on rapid access to high-quality and interpretable large datasets. Scientific publications contain a tremendous wealth of relevant data and these can possibly, but not certainly, be used to develop reliable AI algorithms useful for battery R&D. To address this, we present here a text mining study wherein we unravel lithium-ion battery researchers' habits when reporting results, reason on how these habits link to issues of lacking reproducibility and discuss the remaining challenges to be tackled in order to develop a more credible and impactful AI for battery R&D.
Since recent years, Artificial Intelligence (AI) is raising the battery community expectations to revolutionize the way we search for new materials, optimize interfaces and operation conditions, as illustrated by the roadmaps of several international research initiatives, such as the European Battery 2030 +. [3] [12]  They have tremendous capabilities of automatically mining and finding patterns in very large datasets (Big Data) revealing difficult-to-access information and propose solutions to complex problems. These capabilities have been demonstrated early on in many scientific and engineering fields,  [13] [14] [15] [16] [17] [18]  and start to have significant impact in the battery R&D:  [19]  AI has proven to be useful for battery materials discovery,  [20] [21] [22] [23]  electrolyte formulation (in combination with robotics),  [24]  electrode tomography image processing,  [25]  electrode design,  [26]  state of charge estimation,  [27]  aging prediction,  [28] [29] [30] [31]  correlating manufacturing parameters to electrode properties,  [32]  and is being considered for the full cell production chain.
[33, 34] I's "understanding" of a problem and prediction accuracy does not rely on natural science-based models and theories; it rather by-passes them by finding correlations and interdependencies between the variables in a dataset through mathematical operations. Therefore, AI's capabilities depend on the veracity and the completeness of the dataset used with respect to the overall complexity of the system under analysis. The number of variables that should be considered for accelerating LIB R&D in practice arises from an unknown but surely complex numerical expression, involving aspects such as materials synthesis, electrode manufacturing, electrochemical performance, recyclability, environmental impact and cost. The experimental approaches and modeling techniques used nowadays allow to investigate a single or few aspects in multiple scales, e.
e. it allowed increasing the accuracy of their TM algorithm in terms of information retrieval. [38] Back in 2010, with a considerably smaller dataset, Blake found that authors report < 8% of scientific claims in abstracts. [39] For the field of LIBs, we have estimated that only ~11 % of the information found in the full texts could be recovered by using the abstracts only (details in the Methods section). Thus, it is clear that mining full-text articles is strongly preferred. (ii) is linked to the widespread digital format today used and it can be a source of errors, as for instance for the conversion from PDF to plain text format for different journals' layout and special characters. Therefore, TM tools should be improved for the sake of building datasets as accurate as possible. Finally, (iii) constitutes a major challenge: AI-based algorithms require wellcurated inputs for their training, but in the scientific literature most of the data (~80 %) is reported as unstructured text.
[40] herefore the TM algorithms need vocabularies and custom dictionaries to assist data identification, extraction and integration. In general, the task of identifying the structured information of interest (also called entity) in texts is based on Named Entity Recognition (NER). The NER method uses existing databases to identify entities and quantify their occurrence. Substantial progress has been made in NER and information retrieval methods for biomedicine,  [41, 42, 51, [43] [44] [45] [46] [47] [48] [49] [50]  while for chemistry the most known and complete databases available are ChemDataExtractor  [52]  and PubChem. [53] However, there are no libraries specific to battery R&D able to identify information such as the features of composite electrodes, the electrolyte used, the cycling conditions, etc.
Another TM approach widely used today is known as word embedding,  [11]  which associates to each word in the text a vector and tries to recognize its semantic/syntax as a function of their surroundings. ] [56] [57] [58] [59]  As a benchmark in the battery community, Huang et al. constructed a database of battery materials electrochemical properties (such as capacity, conductivity or coulombic efficiency) by mining 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor. [60] Furthermore, the potential of extracting information and trends from literature of the emerging allsolid-state batteries (ASSBs) field was recently highlighted by Randau et al. , which required the use of approximations and hypotheses to manually analyze a small dataset of about 30 publications.
[61] Needless to say, such in depth analysis would not be possible for a much more established technology such as LIB, for which thousands of reports must be individually screened: under the hypothesis of reading 200 articles per year, a researcher will need almost 140 years to read all the LIB scientific publications available today!  [35]  By using an in-house TM algorithm and analyzing more than 13,000 LIB/sodium-ion battery (SIB) scientific publications, we aim in this article to provide a general overview on certain valuable electrode's and cell's features extractable out of scientific literature in terms of how often they are reported and about the scattered ways in which they are reported. Considering the lack of specific datasets enabling the use of approaches such as the word-embedding method, this work is based on a keywords search TM algorithm relying on devoted in-house libraries complex enough to identify several critical information specific to the battery field.
LIB/SIB Scientific Publication Mining: Researchers' Habits Our in-house text-mining algorithm (provided with this Article) automatically extracts information from the full text of peerreviewed publications, as schematically presented in Figure  1. This algorithm is based on a combination of keywords linked with logical operators and devoted libraries complex enough to capture as much as possible the different ways in which the screened properties are reported in scientific literature. In stark contrast, classical search engines embedded in online platforms, such as Web of Science™ or Scopus ® , search for information in the title, abstract, keywords list and references within an article, and do it in a literal way rather than in context. Using a semi-automatic download algorithm, we collected a representative dataset of LIB and SIB scientific publications spanning from 1990 to 2019, comprising ca.
13,000 articles from different journals and editors (Wiley, Springer, Elsevier, American Chemical Society, IOP Publishing and IEEE Xplore) in PDF or XML format. For the former, the text is extracted from both single and double column configurations and the text contained in tables and figure captions is recovered as well. Subsequently, the text passes through a conversion and preprocessing step, in order to transform the PDF format into a computer-readable text format (TXT) and to remove "noise" and useless text such as HTML tags, links and advertisements. The PDF-to-TXT conversion step can introduce errors due to the different templates used by scientific journals, figures embedded within the text, presence of headers and footers, etc. In our case, different Python libraries were tested, choosing a mixed approach between the pdfminer  [80]  and tika  [81]  libraries and an in-house code, as discussed more in detail in section S7.
Batteries & Supercaps Next, a filtering process limits the database to original experimental studies that actually deal with LIBs and SIBs. Two different filters, fully described in section S4, were applied to discard Review articles and to discriminate LIB and SIB studies from other kind of energy storage technologies, such as supercapacitors, K-, Mg-, Al-, Ca-and Zn-Ion batteries, Li-or Naair/sulfur or redox flow batteries, and also articles dealing with LIB/SIB separators. This filter also classifies articles as LIB or SIB, by frequency analysis. A third filter is applied within the Experimental section to discard articles where no electrode composition is found, to avoid considering articles focusing only on materials physical properties, e. g. structural or magnetic characterization of battery materials, or computational studies  [82] [83] [84]  rather than electrochemical performance.
The database was hereby reduced to ca. 6,300 (48 % remaining), ~5,800 LIB and ~500 SIB, articles. Out of the ~6,700 discarded articles, ~7 % were Reviews and ~12 % dealt with other energy storage technologies or LIB/SIB separators, but surprisingly ~81 % were filtered due to lacking electrode composition. The latter comes from: (i) articles without electrochemical testing of composite electrodes e. g. modeling studies, (ii) complete lack of report of the electrode(s) composition(s) or being placed in the Supplementary Information, (iii) data actually being reported, but in a way that did not allow to discriminate it through the search rules defined within the algorithm, as discussed in more detail in sections S5 and S7. While (ii) could (partly) be addressed relatively easily by accessing the Supplementary Information, if the electrode composition is therein reported, (iii) underlines the complexity of recovering information reported in a non-standardized way and to the associated information losses, even when data is reported.
Even if the TM algorithms efficiency is expected to increase, at present and most likely in the next years part of the literature's data will be unreachable, then calling for the need of standardization actions. From each LIB/SIB property of interest a specific library is defined, fully reported in section S9. These libraries should ideally capture all the different ways a certain information can be reported in the literature i. e. being sensitive, while avoiding false hits i. e. being selective. The latter is particularly challenging when dealing with common words such as 'thickness' or 'diameter' or similar formats/units being used to report different kinds of information such as electrode and electrolyte composition. Figure  2  reports the results of our analysis for all the properties investigated (listed in Figure  1 ) for LIBs in terms of how often they are reported in scientific literature.
Some steps have been taken in that direction by the Journal of Power Sources,  [64, 74]  as they have published a series of guidelines and good practices for publishing batteries and supercapacitors research articles. The field of photovoltaics have long been discussing around a standard report sheet/minimal data to report, and some journals have already started to demand the filling of a datasheet condensing experimental conditions, cell properties and main experimental outputs. [75, 76] Furthermore, the standardized approach starts to be adopted in several initiatives at the European level as the European Materials Modelling Council (EMMC),  [77]  European Materials Characterization Council (EMCC)  [78]  or the Photon and Neutron Open Science Cloud (PaNOSC). [79] he use of a standardized template to report experimental data would bring significant advantages to: * The whole battery community, which would benefit from an extremely valuable database generated and updated continuously by the community itself.
These datasets (used in agreement with the specific Journal policies) would have the potential to boost the research of both academia and industry. As a result, data comparison and evaluation of reproducibility will be strongly simplified. * Journals, editors, reviewers and readers, which would benefit of a faster and easier reviewing process in terms of consistency of the reported results and completeness of the experimental information. * The AI algorithms, for which it will be easier to exploit the data. We strongly believe that the adoption of data templates, such as the example reported as supplementary material of this article, will assist in overcoming the broad standardization challenge. In order to reach a widespread use, journals could implement such templates to be used at the submission stage.
These templates would allow the creation of databases from which data could be extracted easily and accurately, and as a consequence, the wide use of AI approaches will be unlocked. Finally, specific templates should be developed and applied for distinctly different energy storage technologies, such as ASSBs and supercapacitors, and for materials synthesis and manufacturing -which is intrinsically more complicated due to the large variety of techniques and instruments used. A new era is awaiting us and we are dreamful on the idea of being more prolific by spending less time deciphering about the veracity and completeness of reported data. With the emergence of AI, scientists will not become robots filling out templates and clicking checkboxes, but the access to reliable and trustful data will enhance scientific inspiration, cleaver reasoning, and innovative theoretical approaches, all of which are key enablers for technological breakthroughs.
Acknowledgements H. E. -B. , P. J. and A. A. F. acknowledge the  ALISTORE European Research Institute  for the funding support of  H. E. -B. Ph. D. thesis. A. A. F. , T. L. , E. P. and M. D. acknowledge the  European Union 's  Horizon 2020 research and innovation programme  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. and P. S. acknowledge  Institut Universitaire de France  for the support. P. J. acknowledges the financial support from the  Swedish Energy Agency "Batterifondsprogrammet " as well as the continuous support from several of  Chalmers Areas of Advance: Materials Science, Energy , and Transport. H. E.
-B. , T. L. , E. N. P. , M. D. , M. M. , P. S. , A. G. and A. A. F. thank the French National Research Agency for its support through the Labex STORE-EX project (ANR-10LABX-76-01 ). All the authors gratefully acknowledge the  RS2E network  for the inspirational discussions that originated this Article and for its contribution at building the publications database used in our text mining analysis. The authors acknowledge  Prof. Jean-Marie Tarascon  for very useful discussions and support.
In addition, we included a number of species to reconstruct a wider phylogeny of the order to provide secondary constraints to date the Nothobranchius phylogeny (Supplementary Table  2 ). We paid close attention to the taxonomic veracity of samples. We found that gene sequences we generated from wild-caught vouchers were sometimes quite different to accessions deposited on Genbank. For this reason, we only used gene sequences we generated from tissue samples of reliable provenance, with the exception of gene sequences of some outgroup taxa and Nothobranchius furzeri, which were obtained from Genbank (accession numbers are shown in Supplementary Tables  1  and 2 ). Many Nothobranchius sequences on Genbank originate from unreliable aquarium stocks, not vouchered specimens, which obviates independent verification of their provenance.
0,  Stamatakis, 2014)  and Bayesian (BI, MrBayes 3. 2. 6, Ronquist and Huelsenbeck, 2003)  approaches on CIPRES. Three heated chains and one cold chain were employed in the Bayesian analyses, with runs initiated from random trees. Two independent runs were conducted with 20 million generations per run; and trees and parameters were sampled every 500 generations. Convergence was checked using Tracer 1. 5  (Rambaut et al. , 2017). For each run, the first 10% of sampled trees were discarded as burn-in. Majority-rule consensus trees were generated from the individual trees retrieved from the Bayesian analyses. Tree files were imported into Figtree  (Rambaut, 2009)  for tree drawing (http://t ree. bio. ed. ac. uk/software/figtree/). For the dating approach we generated a second alignment matrix based on the sequences of the MyH6, Glyt and SNX33 gene regions of a number of outgroup taxa (Genbank, Supplementary Table  2 ).
The sequences of 19 outgroup taxa from the above groups were aligned using MUSCLE v3. 8. 31  (Edgar, 2004a (Edgar, , 2004b ) and manually refined with the sequences of ten key selected Nothobranchius taxa which we had generated representing all clades (see Supplementary Table  2 ). The datasets were aligned as described above, combined in Geneious and partition models determined using jmodeltest.
Taxon sampling, DNA extraction, PCR amplification and gene region sequencing Our extensive taxon sampling for this study included 73 of the 92 Nothobranchius species currently recognized, plus 7 taxa with an affiliation yet to be determined, see Supplementary Table  1. The localities of respective collections are indicated in Fig. 1. Co-ordinate data are not provided for conservation reasons, but these can be supplied to accredited conservation authorities. In all populations more than one individual was used for DNA isolation, and a number of populations were analyzed of some species. Our genotyping of seven Nothobranchius collections within the ranges of widely distributed species has discovered previously unrecognized lineages, which are phylogenetically distinct. Further evaluations of their respective morphological characteristics are in process to confirm if particular lineages represent distinct species (namely N.
(  2019 ) study, we retrieved a basal position for N. virgatus as opposed to their terminal position. We found our COI and ND2 sequences of N. nubaensis, to be identical to the sequences of their N. virgatus. Both species occur in Sudan and Ethiopia. This error was subsequently corrected by  Cui et al. (2020)  after resequencing. However, the species analysed by  Dorn et al. (2014)  could not be verified independently, in the absence of respective voucher specimens deposited in curated museum collections (cf. Ruedas et al. , 2000). In fact, many Nothobranchius sequences in Genbank originate from unreliable aquarium stocks, not vouchered specimens, which obviates robust taxonomic verification. Our expanded and more resolved phylogenetic hypothesis for the genus also has implications for the subgeneric classification in the genus Nothobranchius.
Acknowledgments Specimens for this study were collected in the wild in the African countries in which these fishes occur. The study did not include any species protected by CITES. Most specimens were obtained prior to the legal implementation of the Nagoya Protocol ( 12. 10. 2014 ) and its corresponding  EU Access  and  Benefit Sharing  (ABS) regulations, which came into effect on 9th November 2015. Permissions for specimen collection in the wild and exportation of samples were granted for the field trips (A): In 2013, to the  Democratic Republic of Congo , with permit number  003/706/INS/AGRI. PEL/KAT. 2013 ;(B). In 2016, to the  Democratic Republic of Congo , with research permit number  FAC/ AGRO/37/2016  and exportation and health certificate number  704/22/ SQAV/CP/HKAT/2016 ; (C) in 2019, to Uganda, Tanzania and  Kenya  with research permit number  CONT/NIC/CMY/1569634  and with health certificate number  2019/CC/OPS/D3/00006  issued to the  Royal Museum for Central Africa, Tervuren, Belgium.
97% identity, which harbour Y-specific genes expressed in testes and con- sidered to be essential for spermatogenesis  [15]. It is apparent that gene conversion is responsible for maintaining the homology between the arms of such palindromes, and therefore the integrity of the sequence and gene functionality in the absence of meiotic recombination between homologs  [16]. A common task, carried out by many international consortia, consists in providing a complete description of common genetic variation by applying whole-genome sequencing to a diverse range of subjects from multiple populations  [17]. Therefore, new and qualitatively distinct computational methods and models are required to utilise the full potential of such broad datasets. One such key example of a computational paradigm shift is indicated by new representations of genomes as graphs  [18]  or as degenerate sequences  [19]  encoding the consensus of multiple sequences, marking a transition from their previous representation as regular sequences.