dataesr/scientific-paragraphs-categorization huggingface dataset:
All data files are transferred to researchers who identify missing or erroneous information. Additionally, quality control procedures exist for downstream sample processing to ensure the integrity of microscopy and genetic samples. To date, samples are being processed and form the basis of two PhD theses. Once published in scientific journals (e. g. ,  Mascioni et al. , 2019) , the environmental data will be sent to a long-lived repository (e. g. , BCO-DMO), and metadata will be deposited in the Antarctic Master Directory. Genetic sequencing data will be stored in online databases (e. g. , GenBank BioProject).
STEP 5: SUSTAIN AND IMPROVE To sustain and improve a CS project, there must be communication among the scientists, operators, staff, and travelers. In addition to coordinating logistics and analyzing samples for publication, researchers must be available to solve issues that occur during the season, gauge strengths and weaknesses of the program, share results in a timely manner, and secure funding to ensure the project's longevity. The impact of the FjordPhyto program is gauged in two ways. First, are samples scientifically relevant and do they advance knowledge of this region? Peer review will help determine whether the data are of sufficient quality to provide scientific merit (e. g. ,  Mascioni et al. , 2019). Second, is there a positive impact on travelers' participation and learning? The latter question is partially evaluated by distributing questionnaires to staff and travelers, asking for feedback and suggestions for improvements.
DISCUSSION After three years of operation, we consider FjordPhyto a successful CS project, proven to be feasible in the field. It provided scientifically relevant samples, and it was well received by IAATO operators and travelers (Table  1 ). We attribute its success to the efforts of dedicated staff and scientists who provided travelers with a meaningful experience. When scoping the problem, we started by setting realistic goals regarding the project's capacity, scale, participant interests, and scientific priorities. Due to the difficulty in collecting data from this region, we consider CS a beneficial approach to addressing questions on phytoplankton community succession, and further, relating that to meltwater over the growth season. Although samples are limited to ship-visited locations, involving more ships can extend the spa- tial and temporal sampling.
, 2013)  (Figure  S1 ). The NEEM-2011-S1 core is a shallow parallel core to the NEEM main core. The estimated age difference between the main ice core and S1 ice core is less than ~5 yr  (Sigl et al. , 2015) , smaller than the age uncertainty resulted from wiggle matching CH 4 records (~±20 yr). No high-resolution CH 4 data exist below 408. 96 m (291 CE)  (Rhodes et al. , 2013). We estimated the gas age for the deeper NEEM ice by linear extrapolation of the depth-gas age relationship at depth of 338-408 m.
II. Literature review 1 Source Thomson Reuters Eikon database. Since its first trading in late 2010, the price of Bitcoin witnessed a substantial rise and rapid growth associated with higher volatility. Numerous factors have been discussed in the literature to explain the determinants of Bitcoin price. Wijk (2013)   The rise in Bitcoin's attractiveness for investors is due, among others, to the low transaction fees. Kim (2017)  analyzed Bitcoin quotes in 16 different currencies and found the Bitcoin's transaction cost is lower than that of retail foreign exchange markets. Bid-ask spreads in Bitcoin markets are found to be 2% less than that of retail foreign exchange markets. The author explains this cost advantage by the virtual infrastructure that Bitcoin uses. Ciaian et al. (2016)  argue that Bitcoin can be considered as any other commodity and, therefore, its price is determined by markets forces of supply and demand.
III. Data and methodology Our sample consists of 21 developing countries namely Argentina, Brazil, Chile, Colombia, Dominican Republic, India, Indonesia, Iran, Kenya, Malaysia, Mexico, Morocco, Nigeria, Pakistan, Peru, Philippines, Singapore, South Africa, Tanzania, Thailand and Venezuela. These countries have been selected based on the availability of data. For each selected country, we use weekly data on local Bitcoin trading volume, the price of two altcoins which are Ethereum and Ripple, and the banking system access measured by the proportion of people aged +15 having a debit or credit card. Ethereum and Ripple have been chosen since they are listed as the second and the third respectively (after the Bitcoin) in terms of market capitalization. Moreover, their price data cover the entire study period unlike other cryptocurrencies (Litecoin, EOS, IOTA) which have price data available for shorter period.
These data are extracted from Thomson Reuters Eikon database and cover the period spanning from August 1 st , 2015 to June 2 nd , 2018 2. To analyze the relationship between the above variables, we employ Vector Error Correction Model (VECM) and AutoRegressive Distrubuted Lag Model (ARDL). VECM is applied when all variables have the same integration order and it exists at least one cointegrating relationship between them. This model provides a convenient way to deal with both short-run and long-run relationships between variables. We estimate a VECM as follows 3  : ∑ ∑ ∑ ∑ = = = - = - - - - + + ∆ + ∆ + ∆ + ∆ + = ∆ p i p j p k t t p l l t l k t k j t j i t i t ect BKG ETH XRP TDG TDG 1 1 1 1 1 1 1 4 3 2 1 0 ε δ α α α α α (1) ∑ ∑ ∑ ∑ = = = - = - - - - + + ∆ + ∆ + ∆ + ∆ + = ∆ p i p j p k t t p l l t l k t k j t j i t i t ect BKG ETH TDG XRP XRP 1 1 1 2 1 2 1 4 3 2 1 0 ε δ β β β β β (2) ∑ ∑ ∑ ∑ = = = - = - - - - + + ∆ + ∆ + ∆ + ∆ + = ∆ p i p j p k t t p l l t l k t k j t j i t i t ect BKG XRP TDG ETH ETH 1 1 1 3 1 3 1 4 3 2 1 0 ε δ λ λ λ λ λ (3) ∑ ∑ ∑ ∑ = = = - = - - - - + + ∆ + ∆ + ∆ + ∆ + = ∆ p i p j p k t t p l l t l k t k j t j i t i t ect ETH XRP TDG BKG BKG 1 1 1 4 1 4 1 4 3 2 1 0 ε δ θ θ θ θ θ (4) Where ∆ is the first-order difference operator, TDG denotes the local Bitcoin trading volume, XRP is the Ripple price, ETH is the Ethereum price, BKG is the percentage of people aged +15 having a debit or credit card, and ect is the error correction term (the residual obtained from the cointegrating equation).
Germination experiments Both the greenhouse and the natural experiments had a randomised block design with seven blocks (corresponding to sampling month) and four treatments (corresponding to animal vector). Over a 15-month period, we recorded the germinated seedling species weekly and then removed them. To obtain seedling species richness and abundance for each sampling season (spring, summer, autumn), we pooled the monthly data from May-June, July-September and October-November for each site and each animal vector.
Greenhouse data analysis We used generalized linear mixed models (GLMM) to compare seedling abundance and the number of species among dispersal vectors, season and habitat type (dung sample as the statistical unit). Negative binomial and Poisson regression models were respectively assigned for seedling abundance and species richness (count response variables) including additional over-dispersion in the model. Animal species (4 species), sampling season (spring, summer and autumn) and habitat (forest and steppe-forest ecotones) were fixed factors, and site within habitat was a random effect. The log-transformed weight of each dung sample was taken as an offset to account for differing sample weights. We used the lsmeans package to obtain the predicted values for each combination of factors.
Data analysis for natural versus greenhouse conditions We used Poisson regression models to compare seedling abundance and species richness among animal species and between germination conditions (greenhouse vs. natural). First, we fitted the full model to include all the main factors and Animal species × Germination conditions interaction, with dung sample as the statistical unit. Best model selection was based on the lowest Akaike Information Criterion value (AIC). The lsmeans package and Tukey post hoc test were used to obtain the predicted values for each combination of factors and for pairwise comparisons, respectively. Pairwise comparisons between greenhouse and natural conditions for seedling abundance of common plant species were made with the nonparametric Mann-Whitney U test. We used canonical correspondence analysis (CCA) to compare the composition of germinating plants (square root of seedling abundance for each plant species) among the animal vectors and between germination conditions.
Acknowledgements We are grateful to  Hossein Akhani  and  Atefeh Ghorbanalizadeh  for their help in the field and their assistance with plant species identification. We also thank  Esmail Ghadimi ,  Mojtaba Ghadimi  and  Javd Pourrezaei  for assisting with data collection,  Vicki Moore  for the English revision of the manuscript, and four anonymous reviewers for valuable comments on an earlier version of the manuscript.
Results A 1 H NMR metabolomic analysis was performed on plasma, urine, and amniotic fluid collected from 611 fetuses at 90 and 110 days of gestation. Metabolic quantification was performed automatically with the R package ASICS. Among the 190 available metabolites in the reference library of the ASICS package, about 65 metabolites were identified in each fluid (i. e. , 63 in plasma, 64 in urine and 68 in amniotic fluid; see Supplementary Table  S1  and Supplementary Fig. S1 ). Thirty-nine metabolites were identified in all three fluids including many amino acids (e. g. , glutamine, glycine, proline, and arginine) and many sugars (e. g. , glucose, fructose, glucose-6-phosphate). Other metabolites were identified in only one or two fluids, e. g. , leucine and isoleucine, identified only in plasma and urine, or reduced or oxidated glutathione, identified only in urine and amniotic fluid.
Ethics Statement All the fluids from pig fetuses were obtained in the framework of the PORCINET project (ANR-09-GENM-005-01, 2010-2015). The experiment authorization number of the experimental farm GenESI (Pig phenotyping and Innovative breeding facility, doi:10. 15454/1. 5572415481185847E12) is A-17-661. The procedures and the animal management complied with European Union legislation (Directive 2010/63/EU) and the French legislation in the Midi-Pyrénées Region (Decree 2001-464). The ethical committee of the Midi-Pyrénées Regional Council approved the experimental design (authorization MP/01/01/01/11).
Metabolite identification and quantification To measure the concentration of metabolites in the three fluids, NMR metabolomic spectra were processed with ASICS, a recently developed R package  60. Before quantification, spectra were normalized by the area under the curve and aligned with preprocessing functions available in the Bioconductor R package ASICS 60 (version 2. 0. 0). The metabolites in all fluids were identified and quantified using the ASICS method available in the same package. Quantification was performed using the default reference library provided in the package and was processed independently for each fluid but the same maximum chemical shift, set at 0. 02, was allowed for each. The library alignment was improved (compared to that described in Lefort et al. 60  ) by using a global quality control criterion: the correlation between quantifications and targeted buckets of the spectra was maximized to choose the best alignment between peaks.
Pathway enrichment Pathway enrichment analysis was performed with the web-based tool suite MetaboAnalyst 64 (version 4. 0) with the MetPA module  65. Sus scrofa pathways were not available in MetaboAnalyst, so the Homo sapiens KEGG pathways were used, as a reference instead. We also checked the differences between human and pig pathways on the KEGG database and the two pathways were found to be almost identical. This confirmed the relevance of using the human pathways in MetaboAnalyst. Finally, hypergeometric tests were performed to extract pathways enriched in influential or differential metabolites and p-values were corrected for multiple testing using the Benjamini and Hochberg approach. This analysis was carried out for each fluid and on OPLS-DA and mixed model results independently.
Acknowledgements This project received financial support from  French National Agency of Research  ( PORCINET grant   ANR-09-GENM005 ). The PhD fellowship of Gaelle Lefort work was supported by the  French National Research Agency  under the Investments for the Future Program, referred as  ANR-16-CONV-0004 , by  INRAE Mathematics and Computer Science Division , by  INRAE Animal Genetics Division  and by  INRAE Animal Health Divison. The funders had no role in the study design, analyses, results interpretation and decision to publish. The authors are very grateful to the staff of experimental pig facilities (INRA, 2018. Pig phenotyping and Innovative breeding facility, doi: 10. 15454/1. 5572415481185847E12 )and  Marie-Christine Père  ( PEGASE, INRAE ) for expert assistance in surgeries. They also thank all the participants from  GenPhySE ,  PEGASE  and  GenESI laboratories (INRAE)  for sample and data collection.
Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale imaging repositories, facilitating data aggregation across studies.
Genetic data sharing is however different given the sensitive personal information involved, which must, therefore, be shared through secured and controlled access. Brain imaging, by contrast, is often shared via open data repositories, or authorized access. This has led to different approaches in data sharing for brain imaging genetics: fully secured and controlled for all data (e. g. , UK Biobank https://www. ukbiobank. ac. uk/about-biobank-uk/) vs splitting data with open access to brain images but secured access for genetic data (e. g. , Human Connectome Project http://www. humanconnectomeproject. org/). The former approach works for large homogenous projects requiring heavy data management while the latter approach is easier, especially for multiple individual smaller studies or multicentric studies with heterogeneous data collection.
The Brain Imaging Data Structure (BIDS) describes a way of organizing neuroimaging and behavioural data using dedicated names and dictionaries, documenting metadata  [5]. Over time, extensions are being developed and integrated to address users' needs. Here we present the BIDS genetics extension. The primary goal of the BIDS genetics extension is to link BIDS datasets to associated genetic data, especially those existing in separate repositories. The sec-ondary goal is to provide a succinct description of the type of genetic data available, thus enabling searches through multiple imaging datasets.
The Brain Imaging Data Structure Genetic Descriptor Data organized according to BIDS have a rigid folder structure and naming convention. Every dataset comes with a dataset description. json file that contains information relative to authors, funders, ethics, licence, and so forth. To refer to associated genetic data, this file must now include the URL pointing to the genetic data, and optionally the URL of the database, and other associated materials such as dataset descriptor articles. This will make it possible to search quickly through BIDScompliant repositories for datasets with associated genetic data, possibly allowing automatic downloads provided user creden- tials are given, because genetic data are usually under controlled access. Another requirement of BIDS is that a participants.
Extension characteristics and imaging genetic information This extension of the BIDS project aims to help researchers to structure their molecular (multi-level) and imaging datasets side by side to improve data linkage and search performance. To facilitate metadata search, a genetic info. json file must be associated with a BIDS dataset describing which type of genetic information is available. Among the multiple available fields, it minimally requires the keys GeneticLevel, describing which genetic analyses were carried out, and the SampleOrigin. Key values for these fields are "genetic," "genomic," "epigenomic," "transcriptomic," "metabolomic," or "proteomic"  [6]  (Fig. 2 ), and "blood," "saliva," "brain," "csf," "breast milk," "bile," "amniotic fluid," or "other biospecimen. " If the SampleOrigin value is "brain", it is further recommended to add the TissueOrigin field (values: "gray matter," "white matter," "csf," "meninges," "macrovascular," or "microvascular").
This can further be refined by indicating the CellType field with values taken from the cell ontology  [7]  and, if the TissueOrigin is "gray matter", "white matter", or "csf" (cerebrospinal fluid), using the BrainLocation field (values being either MNI coordinates or labels from the Allen Brain Atlas  [8] ). A last, recommended, field is the AnalyticApproach, i. e. , the sampling methodology. This is of particular importance because it indicates in greater detail the type of genetic data available using values from the database of Genotypes and Phenotypes (db-GaP -https://www. ncbi. nlm. nih. gov/gap/). As an example, the single-nucleotide polymorphism (SNP) genotyping (Array) and whole-genome sequencing approaches both provide a wholegenome level of genetic information, albeit with some critical differences.
Conclusion BIDS is an openly developed, community-led standard to name, document, and organize human brain imaging data, allowing FAIR data sharing  [9]  and the automation of complex data preprocessing. In just 4 years of existence, it has revolutionized data sharing and analysis in neuroscience, from a wide adoption and a reference for publications to supporting data repository architectures, and it is critical to many open source analysis pipelines. Here, we present the genetic extension that is integrated into the BIDS specification, providing a full documentation of the available fields, along with online examples and a Javascript validator to ensure that datasets are compliant. By adding a genetic descriptor for imaging data, we hope to facilitate data mining to constitute large multi-scale heterogeneous analysis human datasets that reflect human variability, necessary to enhance our understanding of genetic influence on brain phenotypes.
Availability of Supporting Source Code and Requirements Project name: Brain Imaging Data Structure Project home page: https://bids. neuroimaging. io/ BIDS genetics extension: https://bids-specification. readthedoc s. io/en/stable/04-modality-specific-files/08-genetic-descriptor. html and https://github. com/bids-standard/bids-specification/ blob/master/src/04-modality-specific-files/08-genetic-descript or. md Operating system(s): Platform independent Programming language: Markdown and JavaScript Other requirements: Node. js to run the validator locally or a web browser to run the validator online RRID:SCR 016124 License: CC-BY.
The recent development of LAser Shock Adhesion Test (LASAT) as quantitative Non-Destructive Testing (NDT) process for evaluation of structural bonded assemblies brings new challenges. Applicative assemblies composed of complex materials with poor transverse mechanical properties and highly resistant bonded joints require laser parameters optimization and a more accurate control on the whole process. The development of a numerical tool is then necessary to ensure laser parameters specification to evaluate the bond mechanical strength for a given assembly. In this document, the ability of ESTHER code for the description of laser-matter interaction on aluminum and ablation pressure prediction is exposed. The influence of the target initial reflectivity on ablation pressure is investigated. In this paper, validation of the code in both direct (1 -500 ∕ 2 ) and water-confined (0, 2 -7 ∕ 2 ) irradiation regimes is achieved with comparison to suitable sets of experimental data.
No existing numerical laser-matter interaction models have proven to be predictive in the here-above mentioned configuration of interest. A previous numerical model, developed for mono-pulse ablation pressure prediction, produced results in good accordance with experimental results  [21, 22, 23]. However, it doesn't describe shock waves propagation on -thick samples and was never evaluated in double-pulse configuration. It is thus inappropriate for the here-above described application. The development of such numerical models for both direct interaction regime and WCR is shown here. The numerical tool used in this study for model development is ESTHER code. It is a Lagrangian mono-dimensional code able to describe radiation-matter interaction and shocks propagation in targets. It was initially developed at the CEA/DAM/DIF for femtosecond laser-matter interaction modeling  [24, 25, 26].
It has also been used to simulate nanosecond laser-matter interaction in a high intensity range  [27]. In our intensity range, a previous study was led with this code to highlight the influence of material properties on the ablation pressure determination process based on rear Free Surface Velocity (FSV) measurements  [28]. This previous study also enabled to demonstrate ESTHER capacity of ablation pressure prediction on aluminum in the 50 -500 ∕ 2 range, identified as the typical range used in laser-induced shock wave experiments. This paper presents Esther code validation at even lower intensities to cover the 0 -500 ∕ 2 range in direct regime and the 0 -7 ∕ 2 in WCR on aluminum. The influence of physical phenomena on the ablation pressure is exposed and comparison of laser-matter interaction models with previous results is also achieved in both direct and WCR regimes.
Tabulated EOS establish relationships between density, pressure, temperature and internal energy. Associated data are representative of matter state in conditions far from ambient ones, typical of lasermatter interaction-generated plasmas. These tabulated EOS take also into account solid/liquid and liquid/vapor state transitions. Bushman-Lomonosov-Fortrov EOS (BLF) and Sesame EOS transmitted by the Los Alamos National Laboratory (LANL) are very close to each other in the case of aluminum. Minor divergences exist around state transitions but the effects of such differences are negligible in the considered regime of intensities. These two EOS can thus be employed to model hydrodynamics in matter subjected to significant changes due to laser illumination on the target front face.
Energy transfer In ESTHER, two types of energy transfer phenomena are implemented: thermal conduction and radiative transfer. Thermal conduction is driven by conductivity, calculated at interfaces as an harmonic mean, following Equation  12 , where ℎ , + 1 2 is the mean conductivity at the considered interface, ℎ , the conductivity of one cell and ℎ , +1 of the cell on the other side of the considered interface. Thermal conductivity ( ℎ ) is determined in each cell, taking into account matter state. In solid state, data are extracted from Y. S. Touloukian tables  [32]. In plasma state, data are determined from tabulated data, generated by the atomic physics SCAALP code  [33] ,  [34]. Thermal conductivity can thus be calculated for a wide range of matter states, enabling the description of thermal conduction in the plasma and beyond, in the ablation zone.