dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgment Both IMA and ELS are part of the ASPERA-3 experiment on the  ESA Mars Express spacecraft. We are indebted to all of the involved national agencies and to the  European Space Agency  for conducting the  Mars Express program , especially the  Swedish National Space Board , CNRS in France, and  NASA  (contract  NASW-00003 ) in the USA. EUV values at the Earth is provided by  NASA 's TIMED/SEE experiment. The sunspot numbers are provided by the  Royal Observatory of Belgium, Brussels. The authors thank  J. Sharber  for valuable comments. The lead author (MY) wishes to thank the social welfare (personal assistant) program in Sweden for providing assistance to physically disabled individuals which made it possible for him to conduct this research.
Ficus subsection Urostigma as currently circumscribed contains 27 species, distributed in Africa, Asia, Australia and the Pacific, and is of key importance to understand the origin and evolution of Ficus and the fig-wasp mutualism. The species of subsection Urostigma are very variable in morphological characters and exhibit a wide range of often partly overlapping distributions, which makes identification often difficult. The systematic classification within and between this subsection and others is problematic, e. g. , it is still unclear where to classify F. amplissima and F. rumphii. To clarify the circumscription of subsection Urostigma, a phylogenetic reconstruction based on four nuclear DNA markers (ITS, ETS, G3pdh, and ncpGS) combined with morphology and leaf anatomy is conducted. The phylogenetic tree based on the combined datasets shows that F.
) were included as outgroup representing the first diverging lineage of Ficus as currently understood  [1]. Dried leaf samples from 37 herbarium collections and 26 leaf samples dried on silica gel were used for DNA extraction(for voucher information see S1 Appendix). The silica gel samples together with vouchers were collected in non-protected areas for the access of which no permits were needed (no specific permissions were required for these locations/activities and the field studies did not involve endangered or protected species); see Table  1  for localities. The species involved are non-CITES protected. DNA sequence data were sampled for four nuclear DNA markers (ITS, ETS, G3pdh, ncpGS). In total, 234 sequences were used in the analysis, including 199 new sequences and 35 sequences downloaded from GenBank.
DNA sequence alignments Sequences were initially edited and improved by eye using CodonCode Aligner (CodonCode Corporation, Dedhem, USA) and MacClade 4. 08 OSX  [19] , and both forward and reverse sequences were assembled. All assembled sequences were blasted via GenBank database to check for possible contamination with non-Ficus DNA. The alignment of whole sequences was done online with Phylogeny. fr, option MUSCLE  [20] , and SeaView 3. 2  [21]. Gaps were treated as missing data and indels were excluded from the alignments, because they were not informative or only supported clades that already received high support. Missing markers were also coded as missing data.
Morphological and leaf anatomical data The morphological data matrix was constructed using the most recent taxonomic revision of Ficus subsection Urostigma  [5]. The specimens used in the revision were also the primary source for compiling the data matrix. In addition, specimens, stored in L, representing the species from other infrageneric taxa were also used to score data. In total, 43 qualitative morphological characters were coded for analysis (see S2 Appendix for characters, and S3 Appendix for the data matrix). The leaf anatomical data are based on recent work by Chantarasuwan et al. [16] , to which the character states of non-subsect. Urostigma species were added, either studied (F. cf. rumphii) or extracted from Berg and Corner  [7]. In total 23 qualitative characters were coded for analysis (see S2 Appendix for characters, and S3 Appendix for the data matrix).
Phylogenetic analysis In total five analyses were made. The analyses of the four combined molecular DNA markers were performed with Maximum Parsimony (MP) and Bayesian Inference (BI) methods. The morphology and leaf anatomy dataset was analysed under Maximum Parsimony (MP). Both datasets, molecular and morphology/leaf anatomy, were subsequently combined (total evidence approach) and analysed under MP and BI. The MP analyses were run using PAUP Ã v4. 0b10  [22]  and heuristic searches with 3000 replicates,ten random taxon additions, tree-bisection-reconnection branch swapping (TBR), Mul-Trees option active, and no more than 10 trees saved per replicate. Branch support was performed in PAUP with bootstrap analyses  [23]  with 1000 replicates and all other settings similar to the phylogeny analysis.
Bootstrap percentages(BS) are defined as high (85-100%), moderate (75-84%), low (50-74%) or no support (<50%). Model selection for the Bayesian analysis was conducted using the model selection tool available through the online HIV sequence database site (http://www. hiv. lanl. gov/content/ sequence/findmodel/findmodel. html) checking all 28 models and constructing the initial tree with Weighbor (default)  [22]. The chosen models were HKY+G for ITS, GTR+G for ETS, HrN +G for G3pdh, and HKY+G for ncpGS (JC for the morphological and anatomical data after the manual of MrBayes  [24] ). The datasets were analysed online using MrBayes v. 3. 1. 2 [24]  with 100,000,000 generations via the Cipres science gateway(http://www. phylo. org). The default values of 4 chains (3 heated, 1 cold, temperatures default) and two parallel runs were used, whereby every 1,000 th cladogram was sampled.
A 10% burn-in was executed after Tracer 1. 6  [25]  was used for each tree file to check whether or not the effective sampling sizes (ESS) of all parameters exceeded 200, indicating that they are a good representation of the posterior distributions. The Potential Scale Reduction Factors (PSRF) in the MrBayes SUMP output were 1 or close to 1, which also indicates correct convergence. Bayesian inference produces posterior probabilities that are relatively higher than the corresponding bootstrap frequencies  [26] , thus we only used posterior probabilities (PP) above 0. 9 as (high) support. TreeAnnotator v. 1. 8. 0 (part of BEAST v. 1. 8. 0 package  [27, 28] ) was used to create a Maximum Clade Credibility (MCC) tree from every run. These did not differ in topology, only somewhat in support. The MCC tree of the first run was selected.
Analysis of DNA datasets Seventy six taxa were included in the combined dataset with varying amplification success for the four DNA regions targeted as also found previously  [1] : 74 taxa provided ITS data, 68 taxa ETS sequences, 53 taxa G3pdh sequences, and only38 taxa provided ncpGS sequences. MP analyses of the separate markers did not show major incongruences in topology, therefore all were united. The combined aligned data matrix was 2674 bp long with 472 potentially informative characters. The MP analysis resulted in 1300 most parsimonious trees (MPTs) with a length = 1636, consistency index (CI) = 0. 68, and retention index (RI) = 0. 78 (all characters included, informative and uninformative). The strict consensus tree (not shown) of 1300 most parsimonious trees (MPTs) contains two major clades.
Acknowledgments The first author wishes to thank the  Royal Thai Government  for a scholarship and to acknowledge the support from the  Thailand National Science Museum  enabling him to study for a Ph. D. in Leiden, the  Netherlands. We are grateful to L, P and WAG for herbarium material,  Hortus botanicus Leiden ,  Bob Harwood  (BKF),  S. Sirimongkol ( BKF ),  K. Pruesapan (BK),and  T. Vernimmen (L) for providing silica dried material for DNA extraction and  J. F. Veldkamp  for nomenclature suggestion. The staff of  CEFE-CNRS  and C is thanked for their assistance in the molecular laboratory and for their general help during visits of the first author. Special thanks to  Sam Bruun-Lund (C)  for providing the unpublished ETS Fig 1 primer,  Charlotte Hansen  ( C ) for sequencing, and  Y. Sirichamorn  (L), staff of the  Thailand Natural History Museum  ( A.
REFERENCE ONTOLOGIES AND APPLICATION ONTOLOGIES The ontologies in the OBO Foundry suite are reference ontologies. They correspond to the basic biomedical sciences, such as molecular biology, anatomy, and physiology, which form the presuppositions of every specialized medical discipline. Reference ontologies are built as nonoverlapping modules, with (ideally) one reference ontology for each basic biological and biomedical discipline. Such reference ontologies are designed for global use; they are built around a strict taxonomical backbone; carefully maintained to keep up with new discoveries; and used by multiple teams of researchers in annotating multiple different types of data and literature. In addition to reference ontologies, however, there is a need for what are called application ontologies-ontologies designed for local use by groups of researchers engaged in specialized studies.
The Ontology for Biomedical Investigations The Ontology for Biomedical Investigations (OBI) is an ontology for describing scientific investigations in biology and biomedicine that has been developed within the OBO Foundry framework. [29, 30] As an OBO Foundry ontology, OBI has been reviewed for its adherence to the OBO principles, which include: Free distribution under a creative commons-by 3. 0 license, open source development supported by a broad community, and a number of technical requirements for seamless interoperability with other OBO projects. Ontology for Biomedical Investigations starts out from the idea that both biological and medical investigations typically involve four steps of (1) collecting specimens (including: enrolling subjects), (2) preparing them in certain ways, (3) generating data by taking measurements, and then (4) working with the data.
Ontologies in Biobanking Further supplementing the work of OBI, ontologies have been developed more recently to support the field of biobanking-an area, which is of special relevance here since many of the types of entities relevant to managing biobanks are relevant also to imaging, including specimens, and methods to prepare specimens like staining. There is a requirement in both cases to associate data and the entities (samples, images) described by these data with demographic and other data relating to patients. Major collaborative efforts have been recently undertaken toward shared infrastructures in biobanking. [34, 35] In particular, the Biobanking and Biomolecular resources Research Infrastructure is addressing the issue of how to resolve the problems of data integration which arise where divergent data representations are used by different groups.
Thus far, because multiple domains and types of equipment and data (and vendors) are involved in the imaging domain, the tendency has been for each system and domain to come with its own specialized domain-specific vocabulary such as RadLex, Radiology Gamuts Ontology (RGO), Open Microscopy Environment (OME), Quantitative Imaging Biomarker Ontology (QIBO), and so on. These vocabularies have been developed independently and often with the domain context implicitly included. Some of these representation systems incorporate a degree of support for semantic technology, and Seifert et al. ,  [42]  and Crespo Azcárate et al. ,  [43]  have described the benefits of such semantic approaches for image retrieval. Unfortunately, the multiplicity of independently developed specialized ontologies is now posing obstacles for data integration and retrieval at precisely the point where, with the enormous explosion in the quantities of data available, facilities for integration and retrieval are most sorely needed.
Digital Imaging and Communication in Medicine Standard 47] Despite its widespread adoption, some parts of DICOM still lack consistent semantics, so that different systems can use DICOM to tag similar elements in different ways, which can affect the consistent sharing of data across different applications. The past several years, however, have seen an increased interest in promoting features of the DICOM standard The OWL file in question has a collapsed hierarchy (all terms are is_a children of the single upper-level term "thing"). No formal definitions are provided, and English-language definitions are often circular (often consisting in simple repetition of the term defined). In some cases, as in the example illustrated in Figure  4 , they are worse than circular. Here the term "old films for comparison" is defined as "obtain previous mammography studies to compare to the present study.
The Open Microscopy Environment The Open Microscopy Environment (http://openmicroscopy. org) provides an ontology and data model for describing 5D image data (X, Y, Z, channels, time), sub-image ROIs, and the light path(s) used to generate these images with optical microscopes. [61, 62] The ontology is expressed in XML Schema and has had several independent software implementations,  [63, 64]  including large-scale image repositories (http://cellimagelibrary. org, http://jcb-dataviewer. rupress. org). In addition to low-level image data and light path descriptions, the OME data model allows for extensibility of structured annotations, ad hoc manual image annotation as well as describing data flow through one or more image analysis modules. The data model provides for organizational hierarchies, where multiple images can be grouped into datasets, and multiple datasets can be grouped into projects.
Images can also belong to multiple datasets, and datasets can in turn belong to multiple projects. Images may contain multiple ROIs, which may in turn contain other ROIs. Each of these "first class" objects can contain named (i. e. , ontologically constrained) annotations with specified primitive types (numeric, text, etc. ). The whole OME data model is divided into multiple branches, including an image, and an equipment branch, as illustrated in Figures  7  and 8  below (taken from the overview at http://www. openmicroscopy. org/). Two mechanisms are provided for storing binary pixel data, resulting in two different file formats. The original specification uses Base64 encoding with or without compression to store binary pixel data within text-based XML files. In the alternative OME-Tag Image File Format (TIFF), the pixel data is stored in binary form in conventional TIFF files while the OME XML meta-data is stored in the image description tag of the TIFF format.
The ability of standard TIFF-compatible imaging software to read OME-TIFF, as well as the compact representation of pixel data it provides, has contributed to the popularity of this format. One shortfall of OME is that it is not structured as an ontology in the now standard sense. A portion of the ontology is available in OWL at http://www. itee. uq. edu. au/ eresearch/projects/fusion/ome [Figure  6 ] (Hunter et al. [65]  ). Unfortunately, this lacks an upper-level architecture of the sort that would allow integration with other clinical or image-related ontologies [Figure  9 ].
OntoNeuroLOG The OntoNeuroLOG (ONL) ontology was developed as part of a project aiming at the integration of distributed heterogeneous resources in neuroimaging. [69] ONL-dataset processing (DP) covers the domain of datasets and the processing of datasets and was designed according to the OntoSpec methodology  [70]  on the basis of the DOLCE foundational ontology. [71] ONL-DP covers more specifically the domain of datasets used in neuroimaging, each such dataset being modeled as an information content entity. The taxonomy of datasets comprises around 70 terms, including T1-weighted-MR-dataset, PET-dataset, segmentation-dataset, parameter-\quantification dataset [Figure  12 ]. ONL-DP also covers image acquisition, characterized by the dataset-acquisition-equipment used to acquire the image data, and image processing, characterized by the types of datasets involved as input or output.
The taxonomy of dataset acquisition and processing includes some 96 terms, including segmentation, registration, and quantitative parameter estimation. This ontology was used during the NeuroLOG project to align heterogeneous data from several legacy image repositories located in France, and also in the design of the OntoVIP ontology developed to provide a vocabulary for annotations that would facilitate the sharing and reuse of medical image simulations. [72] Both ONL-DP and OntoVIP are available on the NCBO bioportal. The ontologies in effect provide extensions of the general-purpose DOLCE ontology for the domain of datasets, including image datasets. They still lack definitions for many of their terms. Unfortunately, very few ontologies in the biomedical domain are developed in the DOLCE framework, which creates obstacles for interoperability between ONL and other efforts.
). In addition to the term image, terms must be found for the downstream results of image processing. Each operation performed on an image generates a new representation of the image data that is inextricably tied to the original pixel values. Image ontology should include terms that can tag representations according to how they were generated, including algorithmic parameters. ONL-DP suggests "dataset" to denote these additional representations, as in phrases such as "biomedical imaging dataset". The long-term goal for this initiative is that it will provide a formal set of terms and definitions needed to characterize in full detail both the histopathological images and the algorithms that operate on them. This characterization will achieve two purposes: (1) It will facilitate the integration of databases collected from different institutions and at different times by ensuring that they are compatible, thus leading to larger and more powerful databases; and (2) it will enable researchers working in the field to share algorithms and results more readily, by creating a shared formal vocabulary with which to discuss their operations.
Organizational Levels Inherent in biomedical imaging is the idea of semantic scales at which data is organized. In a typical histopathology image, for example, the smallest unit of interest is a pixel while the largest is the image itself. However, we can also represent the image by clusters of pixels (as in darkly staining antibodies), nuclear structure, gland or basal membrane structure, tissue regions (stroma vs. epithelium), and so on. A common goal in image analysis is to identify and quantify these organizational structures, and so an ontology must capture the levels of organization as they relate to the inputs to the image operators described above. We will reuse as far as possible the content of existing ontologies such as RadLex. One issue which we will need to address is to explain how we avoid redundancy between ontology-based annotation of images on the one hand and existing data formats-including DICOM and OME-that already include extensive sets of related metadata.
EXAMPLE CASE: QUANTITATIVE HISTOPATHOLOGY IMAGE ONTOLOGY The DICOM community has a long experience of addressing in an effective way the specific needs of each imaging modality (which may be very far away from one another, e. g. magnetic resonance imaging, WSI, radiation therapy, ophthalmology imaging), while avoiding diverging into multiple idiosyncratic directions in face of new requirements. DICOM offers a robust framework for image file interoperability among networks. DICOM supplement 145, for example, addresses some of the issues in the pyramidal representation of cellular imaging data in histopathological whole slide images. DICOM does not, however, deeply address quantitative image analytics at a cellular level of resolution. The semantic definition of both image acquisition parameters as well as quantitative image feature parameters thus remains a critical need in the domain of digital pathology.
The current approach to histopathological image analysis involves independent groups working in isolation trying to solve difficult problems using mostly datasets from their own institutions. It is of the utmost importance to establish protocols for collaboration among image analysis experts and pathology experts allowing them to work together on datasets created at different institutions. Such collaborations are not common, not least because of the lack of infrastructure to support them. http://www. jpathinformatics. org/content/6/1/37 Datasets, algorithms, and outcomes data are not easy to share between different research groups and institutions. We thus propose to build a quantitative histopathology image ontology (QHIO) providing the resources for the creation of annotated bodies of data, which will be discoverable and shareable across institutions.
This, however, is a strong reason to build the ontology now-especially in the area of quantitative digital pathology-where we are still early enough to create a standard that can exert a substantial influence on future developments. To be successful, this will require effective maintenance of the QHIO to ensure that new imaging entities and processing methods are incorporated as soon as they become popular and are implemented in the major imaging software packages used by the research communities. In this regard, it is essential that we become involved both with the traditional manufacturers of imaging equipment (present and active in DICOM) and the research groups developing such research software packages (e. g. SPM, FSL, Slicer in the area of radiological imaging). However, in doing so, it is also essential to remain watchful, and avoid ontologies trying to model specific data structures and syntactic constructs, instead of the salient parts of the underlying reality.
ACKNOWLEDGMENTS The project described was supported in part by Awards Number  R01CA134451  (PIs: Gurcan, Lozanski) from the  National Cancer Institute  and by Number  R56 AI111823  (PIs:  Campos-Neto , Beamer) from the  National Institute of Allergy and Infectious Diseases , the  CNRS interdisciplinary mission MASTODONS  under program CNRS-12-MI-MASTODONS-CrEDIBLE,  Intramural Research Program  of the  NIH, National Institute on Aging  ( Z01 : AG000671-13 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Cancer Institute , the  National Institute of Allergy and Infectious Diseases , or the  National Institutes of Health.
The dataset described in this article pertains to the article by  Hue et al. (2015)  entitled "Primary bovine extraembryonic cultured cells: A new resource for the study of in vivo peri-implanting phenotypes and mesoderm formation"  [1]. In mammals, extra-embryonic tissues are essential to support not only embryo patterning but also embryo survival, especially in late implanting species. These tissues are composed of three cell types: trophoblast (bTCs), endoderm (bXECs) and mesoderm (bXMCs). Until now, it is unclear how these cells interact. In this study, we have established primary cell cultures of extra-embryonic tissues from bovine embryos collected at day-18 after artificial insemination. We used our homemade bovine 10K array (GPL7417) to analyze the gene expression profiles of these primary extra-embryonic cultured cells compared to the corresponding cells from in vivo microdissected embryos.
Here, we described the experimental design, the isolation of bovine extra-embryonic cell types as well as the microarray expression analysis. The dataset has been deposited in Gene Expression Omnibus (GEO) (accession number GSE52967). Finally, these primary cell cultures were a powerful tool to start studying their cellular properties, and will further allow in vitro studies on cellular interactions among extra-embryonic tissues, and potentially between extra-embryonic vs embryonic tissues.