dataesr/scientific-paragraphs-categorization huggingface dataset:
Interfaces to the Data Request The DREQ content is provided as a version controlled XML document complying with the schema, but a range of interfaces are provided in order to make the contents more accessible. The use of XML documents ensures robust portability and allows users to import the DREQ into their own software environments. For users who do not wish to confront the details of the XML schema, alternative views provided by the web site 12 and the python package dreqPy 13. The website provides a complete view of the DREQ content in linked pages, and also a range of summary tables as spreadsheets. These include, for instance, lists of variables requested by each MIP for each experiment. The python package provides both a command line and a programming interface. The python code is designed to be self descriptive.
Every record, e. g. the specification of a variable, is represented by an instantiated class with an attribute for each property defined in the record. For example, if cmv is a CMOR variable record, cmv. valid_min will carry the value of the valid_min parameter for that record. The specification of the valid_min parameter is carried as an attribute in the parent class at cmv. __class__. valid_min, which is a similar instantiated class. For instance, the following code: cmv. __class__. valid_min. type = "xs:float" gives the data type of the attribute and cmv. __class__. valid_min. title gives a short description.
Summary and Outlook The CMIP6 data request, or DREQ, provides a consolidated specification of the data requirements of the 23 endorsed MIPs 14 participating in the CMIP6 process. In doing so, it supports those responsible for configuring simulation output, those developing software infrastructure, and those who are trying to anticipate what may be available before it appears in catalogues. The latter include both those responsible for storage systems, and potential data users. The data request has a complex structure which arises from the inherent complexity of the problem: not only are there many more MIPs and experiments than previous CMIP exercises, but not all modelling centres expect to address all the objectives of individual experiments, let alone all MIPs. This means that the request infrastructure has to handle varying aggregations 12 w3id.
Ongoing Importance The entire CMIP process is predicated on producing data for analysis, informing both science and policy. The central importance of a data request to those goals is obvious, but the underlying obstacles to the construction of a well defined request are often unclear. We cannot take it for granted that the goals of participating science teams will be met without detailed attention to output requirements, particularly when, as in CMIP, so much of the value arises from the interactions between MIPs. This detailed attention is only going to become more important in the future as the diversity of the Earth system modelling community grows and pressure for efficent use of the computing resources needed to carry out advanced simulations and store output become greater. Getting output descriptions right will be crucial to delivering and evaluating scientific benefits, and to developing the necessary infrastructure.
Of particular urgency is the systematic development of forcing scenarios that require institutionalized support so that quality controlled datasets and regular updates can be provided in a timely fashion. In addition, a more operational infrastructure needs to be put in place, so that core simulations that support national and international assessments can be regularly delivered. This includes the oversight, development and maintenance of the data requests, standards, documentation, and software capabilities that make possible this collaborative international enterprise. A specific Resolution seeking the support of the World Meteorological Organization (WMO) to CMIP was presented and approved at the 18th World Meteorological Congress, held from 3-14 June 2019. The Resolution drew WMO Members's attention to the importance of CMIP and its critical role in supporting the global climate agenda.
://doi. org/10. 5194/gmd-2019-219 Preprint. Discussion started: 14 August 2019 c Author(s) 2019. CC BY 4. 0 License. Data Request Sections: the DREQ database is split into the following sections, each taking the form of a database table with the number of records specified in column 2. /doi. org/10. 5194/gmd-2019-219 Preprint. Discussion started: 14 August 2019 c Author(s) 2019. CC BY 4. 0 License.
Acknowledgements. M. N. Juckes is funded by the  UK National Centre for Atmospheric Science ,  EU   H2020  project  IS-ENES3 (824084 ). The early stages of work were part funded by  EU   FP7  project  IS-ENES2 (312979 ). K. E. Taylor  and  P. J. Durack  are supported by the  Regional and Global Modeling Analysis Program of the United States Department of Energy's Office of Science , and their work was performed under the auspices of  Lawrence Livermore National Laboratory 's Contract  DE-AC52-07NA27344. The CMIP6 Data Request is a collaborative effort which relied on substantial effort from the  MIP  teams listed in Table B1. Updates and extensions to the CF Conventions and the CF standard names lists required community consensus which emerged with the help of many regular contributors to the CF discussions, especially  J.
Code and data availability. The current version of the DREQ is available from the project website: w3id. org/cmip6dr under the MIT License (BSD). It is provided a versioned XML document, which can be used directly or programmatically (both command line tools and a python library are provided). The exact version of the DREQ discussed in this paper (01. 00. 31) is available from the Zenodo repository at 10. 5281/zenodo. 3361640. It is also available as a package from the Python Software Foundation at pypi. org/project/dreqPy/1. 0. 31/.
Coarse-grained model Coarse-grained and continuum approaches are successfully applied to study cytoskeletal-motor systems. A well-developed model and simulation package is Cytosim that can be used to simulate flexible filaments together with further building blocks that, for example, act as nucleation sites, bind filaments together, and induce motility or severing  (Nedelec and Foethke, 2007). It has been applied to study-among other processes in the cell-meiosis  (Burdyniuk et al. , 2018) , mitosis  (Lacroix et al. , 2018) , and centrosome centering  (Letort et al. , 2016). A different model that includes MT flexibility, MT polymerization and depolymerization, explicit motors, and hydrodynamics has recently been applied to study mitosis  (Nazockdast et al. , 2017a; Nazockdast et al. , 2017b).
X. tropicalis tadpoles were staged according to Nieuwkoop and Faber  (24). Digital paintings were carried out with BLENDER v2. 8b. (B) Data processing workflow. subjected to 100 bp paired read sequencing on a HiSeq 1500 device. For the comparison between T 3 response at pre-HTP and 6 months old paedomorphs, libraries were multiplexed on 4 lanes on a NextSeq 5000 apparatus. Reads qualities were assessed with the FASTQC toolkit v0. 11. 3 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Axolotl raw data have been deposited on the Short Read Archive under accession numbers SRP067617, SRR810197, and SRR8101977, and X. tropicalis under accession numbers PRJNA240154. For the comparison between pre-HTP and 6 months old paedormorphs, samples were prepared following the same protocol, but subject to conventional 75 bp Illumina single end sequencing (TrueSeq), according to the manufacturer recommendations.
Transcripts de novo Assembly and Evaluation of the Assembly Procedure Sequences were trimmed with AlienTrimmer v0. 4. 0 (28) to remove adaptor contaminants and low-quality sequences (Phred score ≤ 26). De novo assembly was carried out with Trinity v2. 0. 6  (29, 30)  with ≥120 M paired-reads, and contigs shorter than 200 bases were discarded. Assembly with increasing number of paired-reads (5 to 120 M) randomly picked from the initial dataset showed that saturation is reached as early as 50 M reads from tailfin RNA. TRINITY run time options have little effect on assembly statistics. See  (31)  for a detailed description of the assembly and clustering procedures. The "long" reads produced by ONT sequencing of the same RNA samples were FIGURE 4 | T 3 affect a similar network of pathways in both species, despite regulating different sets of genes.
Annotation of Transcriptomes Assembled de novo Assembled reference sequences were aligned to the nonredundant (nr) protein sequence database (NCBI, release date November 2014) and to all X. tropicalis RefSeq protein sequences using BLASTX  (32). BLASTX alignments took 720 days (CPU time) on a server with 16 cores and 64 GB of memory. Matches with a bit-score lower than 100 were discarded. Reference sequences were named according to the best reverse BLAST hit (BRBH, highest score). If several reference sequences were assigned to the same protein sequence, only the longest with the best score was kept. Annotation of Axolotl transcripts was carried out with the BLOSUM45 matrix.
Raw read counts were subjected to a variance-stabilization transformation as described in Anders and Huber  (34)  and followed by Principal Component Analysis (PCA), to partition biological and technological variability of the experiments into covariant components. Ideally, one expects the first components to capture most of the biological variability, with little non-biological effect (i. e. , technical noise). On the contrary, major components poorly connected to biological variables (treatment, animals) would indicate that the dataset is dominated by noise. We found that the first component (72% of the total variance) corresponds to the species-specific variance, the second and third components (PC2, 17. 8% and PC3, 9. 9%) correspond to the TH treatment in one or the other species, thus showing a low level of technical noise.
RT-qPCR Reverse transcription was carried out from 2. 5 µg of total RNA, first mixed with dNTP (2 µL, 10 mM, Invitrogen) and random primers (1 µL, 50 µM, Invitrogen) in a final volume of 12 µL (with H 2 O DEPC, Ambion), and incubated at 65 • C for 5 min. After hybridization, samples were put on ice prior to the addition of 1 µL H 2 O DEPC, 1 µL 0. 1 M DTT (Invitrogen) and 4 µL 5X first strand buffer (Invitrogen). RNAse inhibitors (1 µL, RNAse out, Invitrogen) and reverse transcriptase (1 µL, SuperScript III, Invitrogen) were added and the reaction was incubated at 25 • C for 10 min, followed by an incubation at 42 • C for 40 min. Primer express (Applied Biosystems) was used to design primers (see Supplementary Table  5  for Axolotl and Supplementary Table  6  for X. tropicalis). Primer choice for RT-qPCR validation was optimized by combining coding sequence conservation and the reads distribution along Axolotl reference sequences.
BLASTX alignments between Axolotl reference sequences and the corresponding Xenopus analogous coding sequences were visualized by dot-plot (a few illustrative examples are shown in Supplementary Figure  2 ). This allowed us to directly control that evidences for differential expression are unambiguously located in properly assembled transcripts, and are not an assembly artifact resulting in chimeric transcripts. qPCRs were performed on an ABI 7300 (Applied Biosystems) and analyzed with the Prism 7300 system software (Applied Biosystems). H3f3a and rpl8 were selected as control genes for normalization, using Normfinder (36) (data not shown). H3f3a was used to compare untreated groups vs. T 3 treated groups and rpl8 was used to compare developmental stages. Raw data were normalized on the control gene and on the non-treated sample by the 2 CT method.
Network Analysis The overlap between the sets of DE genes and KEGG pathways (Kyoto Encyclopedia of genes and Genomes database) was carried out with JEPETTO [Java Enrichment of Pathways Extended To Topology  (37) ], a plugin to Cytoscape v3. 2. 0  (38). All KEGG pathways containing at least one DE gene were collected and merged to create a global functional interaction network (see main text for description). Cytoscape was used to visualize the network and compute network properties. Hubs are defined as nodes (i. e. , gene product) with a degree (a. k. a connectivity) higher than 20. Empirical cumulative degree distributions were computed with the ecdf function of the Hmisc R package. The degree distribution in X. tropicalis network exhibit a bimodal degree distribution (not shown). This is due to a complex of 59 DE ribosomal protein coding genes, which are highly interconnected, and as such, are characterized by a high degree.
This is also certainly true for Axolotl, where collagenase3 and stromelysin3 expression is also strongly induced of after 48 h of T 3 treatment  (21). In fact, a fast transcriptional regulation may be a general feature of T 3 response, since it is also true in mice  (39). After differential expression analysis, transcriptomic responses were further characterized by topological analysis of biological networks. We also characterized in Axolotl the transcriptional response naturally occurring at the HTP, or after T 3 treatments at later stages (6 months old). Given that no genome sequence was available for Axolotl at the time of this work, we used RNA-Seq paired-end reads to assemble the transcriptome and generate the repertoire of coding sequences. Importantly, this also helped capture specific transcripts originating from an embryonic tissue.
RNA-Seq reads were then mapped to the reference sequences to measure gene expression. In order to circumvent technological biases, and despite the availability of an improved genome sequence and annotation (40), X. tropicalis RNAs were subjected to the same procedure (pairedend sequencing followed by transcriptome assembly) to generate an equivalent set of reference sequences. In fact, at the evaluation step of the bioinformatic pipeline, the around 25,000 known X. tropicalis coding sequences were used as a gold standard to evaluate the assembly process and set optimal parameters for assembly and clustering (Supplementary Tables  1, 2 ). Coding sequences were then annotated by comparison to X. tropicalis and NCBI's nr databases. Systematic dot-plot comparison of assembled transcripts to homologous sequences together with ONT sequencing of the same RNA samples showed that the chimerism level, a typical artifact of transcriptome assembly, is low (∼2%, Supplementary Figure  1 ) and that the vast majority of transcripts align well to their cognate homolog (see illustrative examples Supplementary Figure  2 ).
Integrated Functional Relationships Between DE Genes To get a functional and integrated view of the molecular phenotype described by our dataset, we took advantage of the KEGG pathways  (42) , a collection of curated functional interactions organized in a number of well-identified pathways. Given that components are often shared between pathways, we undertook to reconstruct a network of all the KEGG pathways having at least one DE gene (i. e. , not all genes are DE in the network). In this network, nodes represent gene products and edges represent functional connections (phosphorylates, activates, represses. ) between them. The first advantage of this approach is to provide an integrated view of the functional interactions between gene products and thus address how T 3 response is orchestrated between biological pathways, and whether this response is different between the two species.
Thyroid Hormones Induce Alternative Molecular Phenotypes in Axolotl and X. tropicalis Despite limited overlap between DE gene sets, a few genes idiosyncratic of TH response (mmp11, fos, klf9)  (7, 47, 48)  are also differentially expressed in the 2 weeks post-hatching Axolotl. This important result not only shows that tailfin responds to T 3 , but also that the transcriptional program is different from the resorption program induced in X. tropicalis tailfin. Tailfin responsiveness to T 3 is further supported by (1) change in gene expression in vivo that parallels endogenous variations of TH levels [this work and Rosenkilde et al. (23) ], and (2) the fact that despite different DE gene sets (see below), the same molecular pathways are mobilized in both species. In fact, the Axolotl tailfin transcriptional response is not limited to this small subset of genes but includes 432 genes, affecting various cellular processes.
The evolution of a regulatory module's output can easily account for the differential recruitment of the Fos-Actg1 subnetwork. Obviously, the step forward would be to address the mechanistic details and decrypt their transcriptional regulatory mechanisms. Unfortunately, our ChIA-PET analysis in X. tropicalis shows that neither fos, actg1 nor the other components of the Actg1-Fos subnetwork belong to the repertoire of genes whose expression is directly controlled by the TH receptor (39 and data not shown), and the transcription factors involved remain to be determined. Thus, even if fos and actg1 are direct TH target genes in Axolotl (which we currently do not know), the comparison between the two species is not trivial. In this work, we show that despite the lack of visible anatomical changes, Axolotl tailfin responds to known endogenous variations of T 3 levels, at the transition between tadpole and paedomorph stages (2 weeks post-hatching).
ACKNOWLEDGMENTS We thank  J. P. Chaumeil ,  P. Durand , and  G. Benisti  for animal care,  M. Tassigny  and  Y. Andéol  for Axolotl eggs,  J. Pedraza  and the PCIA high performance computing platform at  MNHN , and  Mélissandre Meteau  for technical assistance. We also thank  Dr. V. Laudet  for helpful discussion during the course of this project and for comments to the manuscript,  Drs. D. Buchholz ,  R. Denver , and  S. Le Crom  for valuable comments to the manuscript. This work was supported by the  Centre National de la Recherche Scientifique  ( PEPS ExoMod Triton  to LMS), the  Muséum National d'Histoire Naturelle  ( Action Transversale du Muséum Formes possibles, Formes réalisées and Action Transversale du Muséum Cycles  biologiques to LMS, Scientific council post-doctoral position to GK), and the  Muséum National d'Histoire Naturelle  ( Action Transversale du Muséum Blanc XTransVies)  to NB.
STUDY AREA AND DATASET USED The UAV dataset was acquired in a dense urban area located at Khanjarpur area, Uttarakhand, India. The location of the study area is depicted in Figure  1. The study area comprises densely packed buildings, which is a common feature in urban settlements in India. Characteristics of rooftop areas in such urban structures include parapet walls, staircase openings etc. The dataset used for this study was acquired using a DJI Phantom 4 Pro (UAV) with an optical sensor mounted on it. The dataset parameters are tabulated and shown in Table  1. Flight parameters were decided prior to the acquisition so as to ensure the acquisition of images that were ideal for a dense point cloud generation. A total of 102 images were acquired. Pix4D was used for generation of a dense point cloud, and CloudCompare was used for processing the point cloud.
For if the normal is predominantly oriented upwards, then the contribution of the normal vector in z-component will much greater than the xcomponent and y-component. Normals were estimated for the filtered dataset, and the components of the normals were extracted. It was observed that, for the z-component of the normal vector, the components representing the roof portions had a range of 0. 97 to 1. Subsequently, points with zcomponents in this range were classified as roof elements. The z-components less than 0. 97 were classified as facade points. The methodology is shown in Figure  2. The non-ground points consist of trees and buildings. The classifier was trained in such a way that the building edges were also considered in the same class as trees due to the presence of shadows. Similarly, shadows were also present at the staircases to the roofs and between buildings where sufficient illumination is generally absent.
Metabolomics data are difficult to find and reuse, even in public repositories. We, therefore, developed the Reanalysis of Data User (ReDU) interface (https://redu. ucsd. edu/), a community-and data-driven approach that solves this problem at the repository scale. ReDU enables public data discovery and co-or re-analysis via uniformly formatted, publicly available MS/MS data and metadata in the Global Natural Product Social Molecular Networking Platform (GNPS), consistent with findable, accessible, interoperable, and reusable (FAIR) principles. 1.
Results and Discussion Many simple but important questions can be asked using repository-scale public data. For example, what human biospecimen or sampling location is best for detecting a given drug? Or what molecules are found in humans <2 years old? Current metabolomics repositories typically require manual navigation and conversion of thousands of different vendor-formatted files with inconsistent metadata formats, and developing data integration algorithms, greatly complicating analyses. ReDU addresses FAIR principles by enabling users to find and choose files (Fig  1a ). This is possible because ReDU formats sample information consistently via a template and drag-and-drop validator backed by standard controlled vocabularies and ontologies (e. g. NCBI taxonomy, 2 UBERON 3, Disease Ontology 4 and MS ontology), and includes geographical location (important for natural products and environmental samples).
ReDU automatically uses all public data in the GNPS/MassIVE repository that has the corresponding ReDU-compliant sample information. 34,087 files in GNPS are ReDU-compatible including natural and human-built environments, human and animal tissues, biofluids, food, and other data from around the world (Fig  1f ), analyzed using different instruments, ionization methods, sample preparation methods, etc. From the 103,230,404 million MS/MS spectra included in ReDU, 4,528,624 spectra were annotated (rate of 4. 39% with settings yielding ~1% FDR) as one of 13,217 unique chemicals (Table  S1 ). 5, 6, 7 iformity of data and sample information in ReDU enables metadata-based and repository-scale analyses (Fig. 1b-g ). Chemical explorer enables selection of a molecule and retrieval of its associations with the metadata, i.
5, 9, 10 Reanalyzing human blood plasma and serum, urine, and fecal samples, networked 5,053,666 MS/MS spectra (~5. 6% annotated) and included annotations to clindamycin. Clindamycin's (  1  ReDU makes public MS/MS data FAIR and connects MassIVE (a data repository recommended by Nature publishing journal for metabolomics and proteomics data) to GNPS (an analysis environment), thereby integrating public data deposition, sample information curation, and data analysis. PCA was performed on GNPS annotations (level 2/3). 7 Human blood plasma samples (orange) from rheumatoid arthritis patients. Sample points size and color were set using UBERON ontology and opacity was set using NCBI taxonomy filtering (your data, 1. 0; 9606|Homo sapiens, 0. 7; and all other data, 0. 25). (c) Metadata filters were used to select human fecal samples (NCBI taxonomy, 9606|Homo sapiens; UBERON, feces) and launch sample information enrichment.
Chemical explorer was used to select 12-Ketodeoxycholic acid, cholic acid, and rosuvastatin. Lifestages: Infancy (<2 yrs), n=1859; Early Childhood (2 yrs < x ≤ 8 yrs), n=93; Adolescence (8 yrs < x ≤ 18 yrs), n=169; Early Adulthood (18 yrs < x ≤ 45 yrs), n=995; Middle Adulthood (45 yrs < x ≤ 65 yrs), n=933; and Later Adulthood (> 65 yrs), n=325. (d) Metadata filters were used to select human blood, feces, and urine into different groups and launch chemical enrichment analysis. Bilirubin, urobilin, and stercobilin are illustrative of the chemical differences between the groups. Similarly, bacterial cultures of 1423|Bacillus subtilis (n=89), 1280|Staphylococcus aureus (n=49), and 1883|Streptomyces (n=7) were selected into groups using filters. Surfactin-C14 and PyroGlu-Ile were two exemplar chemicals observed to be different between groups.