dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, several accessions of the Cabernet Sauvignon cultivar are maintained in different gene banks of the world, clearly identifiable by the combination of their holding institute and their accession numbers (see the European Vitis Database www. eu-vitis. de, EURISCO eurisco. ipk-gatersleben. de/ or GRIN www. ars-grin. gov/npgs/index. html databases). Some years ago, the plant genetic resources community has proposed to associate to each accession an international Permanent Unique IDentifier (PUID). Recently, in support of this effort, guidelines, a dedicated infrastructure and a revision of the MCPD (v2. 1) have been set up by the International Treaty on Plant Genetic Resources for Food and Agriculture to provide genebanks with these PUIDs (http://www. fao. org/plant-treaty/areas-of-work/global-infor mation-system/doi/en/).
This cultivar identifier is currently maintained by the Vitis International Variety Catalog (VIVC, www. vivc. de) and yet very poorly used in published data sets although it could greatly improve their reusability. Laboratories often develop their own identification system for plant material (cultivars, accessions and derived samples) maintained at their own sites, rather than in coordination with germplasm repositories. The origin of a plant material, whether from a repository or a laboratory, is therefore a mandatory information within any minimal information delivered along with data sets, to avoid confusion in the identification of the plant material. These various identifiers are often poorly used and described in submissions to archives of molecular data, making it hard to cross-reference molecular data and individual materials.
oiv. int) and based upon grapevine standards widely used by the grape community since the 1980s, and its widespread adoption is likely to be critical for the success of the GrapeIS. Genome structure, genome expression and genome variation Many biological data types can be expressed with respect to locations on genomic sequence, allowing that sequence to function as a focal point for the integration of data. Among the most important of these to the grapevine community are genes and genetic markers that are key concepts for genetic and genomic studies and, as a consequence, for data interoperability in plant biology. Comprehensive, regularly updated and curated The VIVC catalog proposes a most frequent variety name (the 'prime name'). However, the only unambiguous way for tagging a variety is the 'variety number' given by VIVC.
For instance, the variety 'Augusta' is described five times in the VIVC database originating from different countries. Each of these entries corresponds to a different genotype and sometimes different species. Another example of the possible difficulties arising from accession names is illustrated below with the accessions corresponding to 'Cabernet franc' in the Vassal collection as retrieved from the GnpIS-Siregal portal of the germplasm collections maintained by the French National Institute for Agronomical Research (INRA). a From the VIVC database (www. vivc. de). b From GnpIS-Siregal (https:// urgi. versailles. inra. fr/siregal/). c From GnpIS-Siregal for Cabernet franc and from VIVC for Augusta. catalogs of grapevine genes and markers would therefore be a very useful tool for the grapevine community.
A nomenclature for grapevine genes has recently been published,  24  but the scientific tools enabling gene identification and characterization, which include new and improved genome sequences, annotation protocols, and methods for functional characterization, are still evolving. Standardization description of gene function and interactions (pathways and networks) is of critical importance to allow the integration of state-of-the-art knowledge from multiple sources. The extent of standardization varies according to data type: for example, data for gene expression is better standardized in databases such as GEO (http://www. ncbi. nlm. nih. gov/geo/) than for proteins or metabolites. For metabolite data, the discrepancies within compound structures, purification protocols, and analysis methods make standardization an especially difficult problem.
In recent years, some new resources supporting standardized metabolite data such as MetaboLights (http://www. ebi. ac. uk/metabolights/) have been emerging. Another interesting effort is The Metabolomics Workbench  44  (http://metabolomicsworkbench. org/) that aims at delivering a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials and training material. In parallel, a grapevine-specific metabolic pathway database was developed using hierarchical schema based on gene ontology and enzyme function (VitisCYC  45  ). But these efforts need to be more widely promoted within the grapevine community as only five experiments from two laboratories and related to Vitis vinifera have been deposited so far in MetaboLights (two related to living tissues and three from wine extracts).
In turn, the PRIDE archive (https://www. ebi. ac. uk/pride/archive/) is the most recognized proteomics database. Another specific database exists for protein data, PhosphoSitePlus  46  (PSP http:// www. phosphosite. org/homeAction. action), fulfiling a complementary role from PRIDE. PhosphoSitePlus is an online resource providing comprehensive information and tools for the study of protein post-translational modifications including phosphorylation, ubiquitination, acetylation and methylation. 46  With genetic marker data, there are similar challenges to those of genes: synonymy, homonymy, the necessity to evolve the linked information in relation with new genomes and new genome versions and in addition, the use of novel increasingly high-throughput technologies. Data that should be captured include the technology that was used for their identification, the initial genetic material from which they were derived and their position on a reference sequence.
There are possible standards that could be adopted to handle this data type, including the Minimal Information about any (x) Sequence (MIxS, http://wiki. gensc. org/index. php?title = MIxS), and the Molecular Marker Ontology developed under the umbrella of Bioversity International (http://www. cropontology. org/ontology/CO_500/Molecular %20marker). So far, most of the currently used markers have been archived at NCBI (dbSNP and dbVAR databases) under early IGGP recommendations. EMBL and NCBI archives are an important sources of recommendations for data standardization in this quickly evolving field. Based on the present review of the practices and possibilities in terms of data management for grapevine, Figure  3  describes different categories of participants that could contribute to a GrapeIS, and the key relationships between them.
The first category of participants are data producers, involved in nucleotide sequencing, metabolomics, proteomics, and phenotyping (increasingly using high-throughput platforms), germplasm repositories and individual laboratories. It is the responsibility of these groups to publish well-formatted data sets with complete metadata and well described measured variables to the second category of contributors, the data repositories. These vary from generically focused, international efforts (for example, Genesys for genetic resources, EMBL and NCBI archives for various genomic data, see Figure  3 ) to smaller, community-maintained repositories, focused on grapevine-specific problems or national datasets  32, 45, [47] [48] [49] [50]  (Figure  3 ). Different categories of infrastructures that should contribute to the GrapeIS and their key relationships.
Conclusions The policies of research agencies all across the world are increasingly enforcing measures aiming at improving the FAIRness of public data based on the statement that sharing precompetitive data is a strong fuel for new discoveries but also for innovations. Indeed, only FAIR data can be easily found by virtually any kind of users and re-used, including in combination with private data. There are several components to be implemented by an initiative such as the GrapeIS to increase significantly the FAIRness of the public data produced by the grapevine research community. First, the GrapeIS has to be developed in the frame of an international consortium aiming at representing the whole community. This will include setting up the necessary networking activities including a platform for discussing the roadmaps to support the development of the GrapeIS and to follow up needs.
A first step has been achieved with the writing of the present paper, authored by members of the IGGP steering committee and domain experts representing 9 countries and 18 public Still, the challenge will be to sustain the initiative through funding mechanisms such as the Research Coordination Networks of the National Science Foundation (USA) or COST Action (EU) for the networking activities and the writing of various aligned collaborative projects to implement or develop dedicated tools and software, produce large curated data sets and so on. Ideally, the implementation of common and clear guidelines toward FAIR data in all the projects developed by the grapevine community, which is, moreover, more and more required by the funding agencies, would already create a favorable ground for the implementation of any distributed information system.
Coordination of Europe's biological data repositories is now being led by the ELIXIR life sciences infrastructure (https://www. elixir-europe. org), which is exploring how to make such resources more sustainable. This is still a difficult challenge, but the use of open standards facilitates the development of softwares by the wider community. If these softwares are also published under open-source licenses, common solutions could emerge that could be adopted by many different repositories, working on grapevine but also for other crops or organisms, reducing the cost compared to a system where every group independently develops a complete, proprietary software stack. In this paper, we have proposed a new resource, the GrapeIS, designed to provide integrated access to diverse infrastructures providing grapevine data, with some guaranties of sustainability of the whole system: the federation of infrastructures, the use of open common standards and the animation and dissemination by the IGGP international consortium.
The last important component for the design of a FAIR compliant sustainable information system will be that it is useful to a large group of diverse users. Like the data producers, users also have an important contribution to make in specifying the data models, the goals of the repositories and of the whole GrapeIS infrastructure. Data users can be very diverse and the priority of the IGGP are the researchers in the field of plant biology in public institutions (which also are the main producers of public data) or in private companies, breeders from the public and the private sector, engineers from extension services for grape and wine production, teachers and students. Some data can also be of interest for growers or for the general public (for example, the catalogs of germplasm collections) and the GrapeIS initiative might in time help as well to transfer more of the knowledge produced by the scientific community to a broader public.
So far, there are 10 grapevine experiments published in the database, which is encouraging in terms of openness of the data given that fewer proteomics than metabolomics experiments are carried out: a search in PubMed with the keywords (grapevine AND (Vitis)) OR Proteom* gather 138 papers from the literature, while the keys words (grapevine AND (Vitis)) OR Metaboli* gather 3270 papers.
For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC approach based on two MEG data sets. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on the standard Mixed Norm Estimate (MxNE) in terms of amplitude bias, support recovery, and stability.
We provide information on the integration of different source orientation constraints  [40]  and discuss specific problems of MEG/EEG source imaging such as depth bias compensation and amplitude bias correction. We present empirical evidence using simulations and analysis of two experimental MEG data sets that the proposed method outperforms MCE and MxNE in terms of amplitude bias, active source identification, and stability. Finally, we compare the proposed approach with the dSPM  [45]  and RAP-MUSIC method  [5]  based on two MEG data sets. Notation: We mark vectors with bold letters, a ∈ R N , and matrices with capital bold letters, A ∈ R N ×M. The transpose of a vector or matrix is denoted by a T and A T. The scalar a[i] is the i th element of a. A[i, :] corresponds to the i th row and A[:, j] to the j th column of A.
As SO N , the MEG/EEG inverse problem is ill-posed and constraints have to be imposed on the source activation matrix X to render the solution unique. By partitioning X into S blocks X s ∈ R O×T , where each X s represents the source activation at a specific source location s over time and across O orthogonal current dipoles, we can apply a penalty term P(X) promoting block sparsity by combining a Frobenius norm per block and a l 0. 5 -quasinorm penalty over blocks. The optimization problem reads: X = arg min X∈R SO×T 1 2 M -GX 2 Fro + P(X) X = arg min X∈R SO×T 1 2 M -GX 2 Fro + λ S s=1 P s (X s ) X = arg min X∈R SO×T 1 2 M -GX 2 Fro + λ S s=1 X s Fro , (2) where λ > 0 is the regularization parameter balancing the data fit and penalty term. Similar to the constraint applied in MxNE  [9] , P(X) promotes source estimates with only a few focal sources that have non-zero activations during the entire time interval of interest.
E. Experimental MEG data We evaluate the performance of MxNE and irMxNE using data from the MIND multi-site MEG study  [57] -  [59]. We use two different data sets from one exemplary subject, auditory evoked fields (AEF) and somatosensory evoked fields (SEF), recorded using the 306-channels Elekta Neuromag Vectorview system. A detailed description of the data and paradigms can be found in  [57] -  [59]. For the AEF data set, we report results for AEFs evoked by left auditory stimulation with pure tones of 500 Hz. The analysis window for source estimation was chosen from 50 ms to 200 ms based on visual inspection of the evoked data to capture the dominant N100m component. For the SEF data set, we analyzed SEFs evoked by bipolar electrical stimulation (0. 2 ms in duration) of the left median nerve.
To capture the main peaks of the evoked response and to exclude the strong stimulus artifact, the analysis window was chosen from 18 ms to 200 ms based on visual inspection. Following the standard pipeline from the MNE software  [60] , signal preprocessing for both data sets consisted of signal-space projection for suppressing environmental noise, and baseline correction using pre-stimulus data (from -200 ms to -20 ms). Epochs with peak-to-peak amplitudes exceeding predefined rejection parameters (3 pT for magnetometers, 400 pT/m for gradiometers, and 150 V for EOG) were assumed to be affected by artifacts and discarded. This resulted in 96 (AEF) and 294 (SEF) artifact-free epochs, which were resampled to 500 Hz. The gain matrix was computed using a set of 7498 cortical locations, and a three-layer boundary element model.
The stability of the source reconstruction was tested using a resampling technique. For each data set, we generated 100 random sets of epochs by randomly selecting 80% of all available epochs without replacement. The noise covariance matrix for spatial whitening was estimated for each subsample using pre-stimulus data (from -200 ms to -20 ms). We applied both MxNE and irMxNE on the average of each random set without orientation constraint. Due to the lack of a ground truth, the source reconstruction performance is evaluated by means of the Goodness of Fit (GOF) and the active set size. To compare results with well established source reconstruction techniques, we compute the dSPM solution  [45]  without orientation constraint and the RAP-MUSIC estimate  [5]  for both data sets using the MNE-Python software  [61].
For RAP-MUSIC, we use single-dipole and two-dipole independent topographies to address the problem of correlated sources  [4]. A similar idea is pursued e. g. by dual core beamformers  [62]. The correlation threshold was set to 0. 95 as proposed by Mosher et al. [4]. The rank of the signal subspace was determined by thresholding the eigenvalues of the data covariance based on an estimate of the noise variance. As we apply a spatial whitening, the threshold was set to 1.
B. Experimental MEG data 1) Auditory evoked fields: We first compare the performance of the proposed BCD scheme for solving the weighted MxNE with the Fast Iterative Shrinkage Thresholding Algorithm (FISTA)  [63] , a proximal gradient method used in  [9]. Both methods were applied with and without active set strategy. All computations were performed on a computer with a 2. 4 GHz Intel Core 2 Duo processor and 8 GB RAM. The computation times as a function of λ are presented in Fig. 2. The BCD scheme outperforms FISTA both with and without active set strategy. Combining the BCD scheme and the active set strategy reduces the computation time by a factor of 100 and allows to compute the MxNE on real MEG/EEG data in a few seconds. Since subsequent MxNE iterations are significantly faster due to the restriction of the source space, irMxNE also runs in a few seconds on real MEG/EEG source localization problems.
Alternatively, post-processing is generally required, e. g. by defining regions of interest, to improve interpretability. Note also that, in contrast to dSPM, source amplitudes obtained with irMxNE are moments of electrical dipoles expressed in nAm, which is similar to dipole fitting procedures  [2]. The GOF of the two-dipole model obtained with irMxNE is 81. 9% and thus only slightly lower than the three-dipole model obtained with RAP-MUSIC. 2) Somatosensory evoked fields: We applied MxNE and irMxNE (with and without debiasing) with different regularization parameters to 100 averaged random subsets of epochs of the SEF data set. The mean GOF and the corresponding active set size for MxNE and irMxNE (with and without debiasing) as a function of the regularization parameter λ are shown in Fig.
6. We can see again that     The number of false positives is also significantly lower. These results confirm the findings obtained from the AEF data set in section III-B1. The stability analysis reveals also that the source in the ipsilateral secondary somatosensory cortex (iS2) is less stable compared to the contralateral sources, which might be caused by its relatively weak field pattern  [12]. Fig. 8  presents source reconstruction results obtained with MxNE and irMxNE for selected regularization parameters. As in section III-B1, we show a RAP-MUSIC estimate and added the maximum dSPM score per source as an overlay to all subfigures. MxNE with λ/λ max = 40% reconstructs dipoles in the contralateral primary somatosensory cortex (cS1), the contralateral and ipsilateral secondary somatosensory cortices (cS2 and iS2), and the posterial parietal cortex (cPPC).
The source locations roughly coincide with the main peaks of the dSPM estimate. As for the AEF data set, the source activation per region is split into several correlated dipoles. An increase of the regularization parameter results in a loss of physiologically meaningful source activity such as activation in iS2, which is visible in the dSPM estimate. The relevance of this activation is also indicated by the drop of the GOF in Fig. 6. The signal subspace estimation for RAP-MUSIC yields a signal subspace size of 43. Hence, the RAP-MUSIC estimate depends on the choice of the correlation threshold. For our settings, four independent topographies, two single-two two-dipole topographies, are above the subspace correlation threshold. Dipoles are reconstructed in all relevant areas. The activation in cS1 is split into several dipoles, probably due to the fixed orientation source model applied in RAP-MUSIC.
(  5 ). The irMxNE method is designed for offline source reconstruction, which is still the main application of MEG/EEG source imaging in research and clinical routine. However, we are aware of a growing interest in real-time brain monitoring  [64]. New techniques such as parallel BCD schemes  [51] , clustering approaches  [65] , and safe rules  [66]  can help to further reduce the computation time. As proposed in  [22] ,  [26] , the first iteration of irMxNE is equivalent to computing the standard MxNE. Consequently, the irMxNE result is at least as sparse as the MxNE estimate. The iterative reweighting procedure can thus be considered as a post-processing for MxNE improving source recovery, stability, and amplitude bias. This was confirmed by empirical results based on simulations and two MEG data sets.
In particular, approaches maximizing model stability are an interesting alternative  [67] ,  [68]. Model selection is however a general issue in MEG/EEG source reconstruction. In our comparison with RAP-MUSIC, we found e. g. that the size of the signal subspace and the correlation threshold have a strong influence on the final source estimate. Due to the limited number of samples, we can only obtain an empirical estimate of the data covariance, which involves the risk of overestimating the size of the signal subspace using the thresholding procedure. The correlation threshold is used to switch to higher order source models and to account for noise components in the signal subspace. Similar to MxNE, irMxNE assumes that the locations of active sources is constant over time. Hence, it should be applied to data, for which this model assumption is approximately true, e.
g. , by selecting intervals of interest or applying a moving window approach. To go beyond stationary sources, the reconstruction of nonstationary focal source activation can be improved by applying sparsity constraints in the time-frequency domain such as in the TF-MxNE  [20]. Preliminary results on the application of non-convex regularization for such models based on iterative reweighting procedures were presented in  [69]. The irMxNE solver is available in the MNE-Python package  [61].
was supported by the  German Research Foundation  ( Ha 2899/21-1 ), the  European Union  ( FP7-PEOPLE-2013-IAPP 610950 ), the  EDF  and  Jacques Hadamard Mathematical Foundation  ( Gaspard Monge Program for Optimization  and operations research), the  French National Research Agency  ( ANR-14-NEUC-0002-01 ), and the  National Institutes of Health  ( R01 MH106174 ). D. Strohmeier  is with the Institute.
Corpus Description The SeeDev corpus is a set of 86 paragraphs from 20 full-text articles, selected by plant biology experts, about seed development in Arabidopsis thaliana. Paragraphs of the same document may be distributed into different sets. The "Documents" row indicates the proportion of documents represented in the set. The SeeDev corpus is smaller than other BioNLP-ST corpora, e. g. a fifth of Cancer Genetics corpus and a third of GENIA corpus. The manual annotation of the SeeDev corpus required a high level of expertise that do not allow for a large corpus, as in many specific domains of Life Science. We identify small dataset processing as a challenge to overcome by information extraction tools. Table  1  details the distribution of instances per relation type in the training, development and test sets of the SeeDev-binary task.