dataesr/scientific-paragraphs-categorization huggingface dataset:
The  Kelley et al. (2013)  scheme does not address key aspects of the coupled vegetation-fire system including the amount of above-ground biomass and/or carbon, fuel load, soil moisture, fuel moisture, the number of fire starts, fire intensity, the amount of biomass consumed in individual fires, and fire-related emissions. Global data sets describing some of these properties are now available, and will be included in the FireMIP benchmarking scheme. These data sets include above-ground biomass derived from vegetation optical depth  (Liu et al. , 2015)  as well as ICESAT-GLAS lidar data  (Saatchi et al. , 2011) , the European Space Agency Climate Change Initiative Soil Moisture product  (Dorigo et al. , 2010) , the Global Fire Assimilation System biomass-burning fuel consumption product, fire radiative power, and biomassburning emissions  (Kaiser et al.
, 2012) , and fuel consumption  (van Leeuwen et al. , 2014). The selection of new data sets is partly opportunistic, but it reflects the need to evaluate all aspects of the coupled vegetation-fire system as well as the importance of using data sets that are derived independently of any vegetation model uses the same driving variables as the coupled vegetation-fire models being benchmarked. The goal is to provide a sufficient and robust benchmarking scheme for evaluation of fire while ensuring that other aspects of the vegetation model can also be evaluated, and to this end new data sets will be incorporated into the FireMIP benchmarking scheme as they become available during the project. The FireMIP benchmarking system will represent a substantial step forward in model evaluation. Nevertheless, there are a number of issues that will need to be addressed as the project develops, specifically how to deal with the existence of multiple data sets for the same variable, how to exploit process understanding in model evaluation, and how to ensure that models which are tuned for modern conditions can respond to large changes in forcing.
Acknowledgements. Stijn Hantson  and  Almut Arneth  acknowledge support by the  EU FP7  projects  BACCHUS  (grant agreement no. 603445 ) and  LUC4C  (grant agreement no. 603542 ). This work was supported, in part, by the  German Federal Ministry of Education and Research (BMBF) , through the  Helmholtz Association  and its research programme  ATMO , and the  HGF Impulse and Networking  fund. The  MC-FIRE model development  was supported by the  global change research programmes  of the  Biological Resources Division of the US Geological Survey  ( CA 12681901 ,112-), the  US Department of Energy  ( LWT-6212306509 ), the  US Forest Service  ( PNW96-5I0 9 -2-CA ), and funds from the  Joint Fire Science Program. I. Colin Prentice  is supported by the  AXA Research Fund  under the  Chair Programme in Biosphere and Climate Impacts , part of the  Imperial College   initiative Grand Challenges in Ecosystems and the Environment.
Fang Li  was funded by the  National Natural Science Foundation  (grant agreement no. 41475099  and no. 2010CB951801 ). Jed O. Kaplan  was supported by the  European Research Council  ( COEVOLVE 313797 ). Sam S. Rabin  was funded by the  National Science Foundation Graduate Research Fellowship , as well as by the  Carbon Mitigation Initiative. Allan Spessa  acknowledges funding support provided by the  Open University Research Investment Fellowship  scheme. FireMIP is a non-funded community initiative and participation is open to all. For more information, contact  Stijn Hantson  (stijn. hantson@kit. edu). The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association. Edited by:  A. V. Eliseev.
Data availability The international disaster database can be accessed at http://www. emdat. be/. The Firecci gridded burnt-area product can be downloaded from https://www. geogra. uah. es/esa/grid. php, the L3JRC global burnt-area product can be downloaded from http://forobs. jrc. ec. europa. eu/products/burnt_areas_L3JRC/ GlobalBurntAreas2000-2007. php, the MCD45 global burnt-area products can be downloaded from http://modis-fire. umd. edu/pages/BurnedArea. php?target=Download and the GFED4 gridded burnt-area data can be downloaded from http://www. globalfiredata. org/data. html.
The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: "biological process", "cellular component", "molecular function" and "disease or syndrome". Results: We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology.
Blood dataset The blood dataset used in this work consists of transcriptomics data obtained by next generation sequencing (NGS) from whole blood. The dataset consists of 33 controls, 27 presymptomatic mutation carriers and 64 symptomatic mutation carriers. The phenotypes associated with this dataset that we used in this analysis were carrier status, CAG repeat length, and two clinical scores namely motor score and Total Functional Capacity (TFC) score. The TFC score is inversely correlated to the motor score and the CAG repeat, and it reflects the disease severity with lower numbers indicating higher severity of the disease. The carrier status was assigned '0' for controls and '1' for HD carriers. The CAG repeat values ranged between 17 and 53, covering both controls and HD samples. The motor score of the control subjects ranged between 0 and 11 and between 0 and 107 for the HD subjects.
The TFC score for the control subjects ranged between 11 and 13 and for the HD subjects between 0 and 13. We used the processed blood data set that is publicly available in the NCBI Gene Expression Omnibus (GEO) database under accession number GSE51799. Details for the dataset can be found in the original publication  [10]. We removed the presymptomatic samples from the blood samples because there were only 3 presymptomatic samples in the brain study. We also removed transcripts with low tag count (less than 94). The dataset was normalized using the calcNormFactors (TMM method) from the edgeR bioconductor package for RNA-Seq  [19]. In addition, the dataset was transformed using the method described in  [20].
Brain dataset The HD human brain data used in this analysis were obtained from the public repository NCBI Gene Expression Omnibus, entry GSE3790. The data set was originally created and analyzed by Hodges et al. [5]. It contains 44 HD gene positive cases and 36 age and sex matched controls. They analyzed three different brain regions, caudate nucleus, frontal cortex (BA4 and BA9 regions) and cerebellum, with an Affymetrix Microarray GeneChip (Human Genome U133A and U133B). In addition, they classified the HD cases based on Vonsattel grade of disease pathology (scale = 0 -4). We used the processed data from the Human Genome array U133A to construct the WGCNA network. The dataset was log2 transformed.
Application of weighted gene co-expression network analysis (WGCNA) The transformed blood and brain datasets were used to construct the weighted gene co-expression networks. We used the original WGCNA algorithm as described in  [15]. The parameters were chosen based on the assumption of a scale-free topology of the co-expression network, according to WGCNA guidelines, and also in order to make modules from each network of comparable sizes. Other parameters followed default settings that are based on earlier investigations of the WGCNA method by Horvath et al. [15, 16]. The soft threshold for each network was: caudate: 9, BA4: 3, BA9: 5, cerebellum: 5, blood: 9. The parameter minModuleSize was assigned to 15 for all networks. We performed module identification using the dynamic tree cut algorithm  [21].
0001. In total we identified 34 modules in the blood dataset consisting of a median of 66 genes (mean:233). In the brain dataset we identified in total 55 modules in the caudate nucleus, 67 in BA4, 118 in BA9 and 81 in cerebellum. Details on the association of modules per network with the disease phenotypes can be found in Additional file 1. Module size per network in the brain datasets were for the caudate median: 105. 0, mean: 335, for the cerebellum median: 75, mean: 275. 1, for the BA4 median: 67, mean: 332. 6 and for the BA9 mean: 51. 5, median: 188. The association of modules with the disease phenotypes was performed with an implementation of the R's standard cor function in the WGCNA package. The correlation method that was used was Pearson correlation.
Module annotation -parameters and semantic types The modules were annotated with concepts of a certain semantic type using the Taverna  [24, 25]  workflow which is publicly available from the myExperiment repository http://www. myexperiment. org/workflows/3921. html. This workflow orchestrates a series of CPA web services for module annotation which use literature that was mined up to July 2012. We used a selection of 4 predefined concept sets from our local database of concept profiles. A concept set is a collection of concept profiles related to a particular semantic type (e. g. biological processes). The concept sets that we used in our analysis were: biological processes (ID:5), cellular component (ID:3), molecular function(ID:4) and disease or syndrome (ID:82). The first three concepts sets are based on the Gene Ontology (GO).
Gene Ontology (GO) terms are often given as words or phrases that are infrequently found in normal texts. To still provide broad coverage of GO terms, we previously developed a procedure specifically for GO  [17]. In short, the GO concept profiles were created based on the literature (PubMed identifiers) provided in the GO database itself. That includes literature based on all gene-GO term associations and GO-terms without an explicit association with genes (version 2012-07-14, direct download link: http://archive. geneontology. org/lite/2012-07-14/go_20120714-assocdb. rdf-xml. gz). The disease or syndrome concept set provides information about diseases or syndromes that currently exist in the UMLS  [26]  and OMIM databases  [27] , by using information from the complete literature. The top 20 annotations (based on the sum of their similarity scores with all of the genes in a module) from each concept set was used to annotate each module.
Randomization approach and significance of the modules The following algorithm was applied separately for each of the comparisons between the datasets and for each semantic annotation category separately. Suppose we have identified n modules A 1 , A 2 ,. , A n in blood and m modules B 1 , B 2 ,. , B m in one of the brain regions. We use CPA to annotate each module and compute similarity scores S ij for all pairs (A i , B j ), based on the number of overlapping annotations. To assess the significance of these similarity scores, we use a permutation-based approach as follows. For k = 1, 2,. 100, we generate sets of random modules A 1k , A 2k ,. , A nk and B 1k , B 2k ,. , B mk of the same size as the original modules A 1 , A 2 ,. , A n and B 1 , B 2 ,. , B m. We compute similarity scores S ijkl for each pair (A ik , B jl ).
Workflow to identify HD modules in blood and common functional signatures between blood and brain In order to identify common Huntington disease-specific signatures in brain and blood, we created groups of co-expressed genes (modules) using weighted gene coexpression network analysis (WGCNA). We first applied WGCNA to the blood and brain dataset and identified modules that were associated with the disease phenotype. We then used Concept Profile Analysis (CPA) technology to annotate each module separately with different semantic annotation categories and assess the similarity between the different networks (blood versus the four brain regions) based on the overlap of the module annotations (Fig. 1 ). The annotations were based on four semantic categories: biological processes, cellular component, molecular function and disease or syndrome.
Disease signatures identified in the blood data set For our functional analysis of HD signals in blood data, we used our blood network modules that were identified by WGCNA. We identified in total 8 modules that were correlated with the disease phenotype from a total number of 34 modules (Fig. 2 ). Each module was annotated using CPA and the most representative annotations comprised the signature of that particular module. The extended list of the blood modules and their associated annotations can Fig. 1  Workflow for the identification of common signatures between blood and brain tissue. The blood and brain datasets are transformed into gene co-expression networks using Weighted Gene Coexpression Network Analysis (WGCNA). The networks are represented by modules, groups of highly co-expressed genes.
WGCNA in the brain dataset We also applied WGCNA to the four brain regions separately to identify modules that are correlated with the disease phenotype. WGCNA has been applied before in the same brain dataset  [39] [40] [41]  but we had to reanalyze this data to obtain module sizes that were comparable with the modules from the blood dataset. We found the highest number of modules associated with the HD phenotype in the caudate nucleus (37 modules). Cerebellum and BA4 follow with 29 and 23 modules respectively (Additional files 1 and 6). BA9 exhibited poor association with HD since none of the BA9 modules were significantly correlated with the disease state. However, a large number of modules exhibited a strong association with the disease staging phenotype (Additional files 1 and 6). Because the semantic annotations of the modules from each brain network can be relevant for future research, we report here the processes that were prominently present in all brain regions: immune response, chromatin remodeling, histone modification, cell-cycle, myelination and cell differentiation (oligodendrocyte, axon and Schwann cells), synaptic activity, protein transport, nuclear activity, protein kinase activity and RNA splicing.
This finding supports previous detection of abnormal activation of immune response in HD patients  [31]. In addition, this signature links the well-established neuroinflammation signature in brain to a parallel inflammatory response in blood, triggered upon expression of the mutant huntingtin. The same signature was also identified by Horvath and colleagues in their study of the preservation of brain modules in two large blood cohorts of healthy individuals  [11]. Although they were unable to identify full module preservation, they found that a subset of the genes that was preserved was functionally enriched in, among others, infectious disease and infection mechanisms. Both analyses point to an immune response mechanism as a shared channel between blood and brain. Although the mechanisms preserved in those datasets came only from healthy individuals, we conclude that this preserved signal is also specific for HD.
The blood modules involved in these signatures that link the blood with the brain pathology could become subject for further research to confirm whether symptoms of the disease can be treated by targeting factors that associate with these modules  [68]. Although the blood-brain signatures that we identified are promising, there are certain limitations in our methodology that can be improved in follow up studies. Considering that similarity was assessed by overlap in annotations, future studies can extend the power of the method by using the hierarchy of an ontology to assign a score to annotations that are subclasses of the same function. Furthermore, the results from the computational analysis can be corroborated by further validation on additional data and by new experiments in the laboratory.
The genetic predictability of HD allows for testing those signatures in carriers of the gene mutation both in mouse and humans, even before the first symptoms arise. We are currently investigating the analysis of data from human blood samples that were collected from the same subjects, but 4 years later as an accurate way of determining whether these signatures have changed over the time and whether they correlate with the progression of the disease. Testing these signatures in mouse models of HD to follow the efficiency of novel disease treatment strategies would also be beneficial for using and optimizing blood as a diagnostic and monitoring tool.
Acknowledgments We gratefully acknowledg the fundings from the  European Commission  ( FP-7  project  RD-Connect , grant  584  agreement No. 305444 ), the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) [grant no. 2012-305121 ] 'Integrated European -omics  research project for diagnosis and therapy in rare neuromuscular and neurodegenerative diseases (NEUROMICS) ' and the  IMI-JU project Open PHACTS  (grant agreement No. 11519 ). We would also like to thank  Frederic Parmentier  and  François-Xavier Lejeune  from the  INSERM institute in Paris  for the fruitful discussions and the  Biosemantics group in Leiden University Medical Center in the Netherlands  for their technical expertise. This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative.
processes. 373 It is important to note that the time resolution of FEEPS (2. 5 s) does not allow 374 measurements within the EDR but rather shows the reversal of the magnetic field-aligned 375 motion of the >50 keV electrons from southward of the EDR to northward of it as MMS2 376 made this traversal. In this sense the FEEPS data provide independent confirmation of the 377 opening of magnetic field lines across the EDR as deduced from the appearance of the 378 parallel crescent in the low-energy electron data. 379 380.
Both systems are effective and both are being used throughout 563 the mission. The data from all the independent sensors on each satellite, and 564 between the four satellites, are intensively inter--calibrated  (27, 28). 565 Beginning on March 1, 2016 the entire MMS data set has been available on--line 566 at https://lasp. colorado. edu/mms/sdc/public/links/. Fully calibrated data are 567 placed on--line at this site within 30 days of their transmission to the MMS Science 568 Operations Center. The data are archived in the NASA Common Data Format (CDF) 569 and so can be plotted using a number of different data display software packages 570 that can use CDF files. A very comprehensive system called the Space Physics 571 spdsw_latest. zip. Training sessions on the use of SPEDAS are held on a regular basis 597 at space physics related scientific meetings.
Acknowledgements. This paper is based on observations collected at the  Observatorio del Teide , operated by the  Instituto de Astrofísica de Canarias (IAC), in the island of Tenerife (Canary Islands, Spain) ; and at the  Nordic Optical Telescope at the Observatory of Roque de Los Muchachos, in the Island of La Palma (Canary Islands, Spain). We are thankful to the astronomers in charge of service mode observing at the  Observatorio del Teide  for the careful execution of our program. F. C. warmly thanks the  Specola Vaticana  for their hospitality during the time when much of this work was done. F. C. and A. P. acknowledge support from the  DFG Research Centre   SFB 881  " The Milky Way System " through project  B5. The authors are grateful to the referee,  Ben Davies , for very useful comments that helped improve the quality of this paper.
Significant wave heights from altimeters Altimeter data have provided an ample source of global wave observations and aided in the development and evaluation of spectral wave models (e. g. ,  Chen et al. , 2002; Ardhuin et al. , 2010; Stopa et al. , 2015). Significant wave heights (H s ) are measured from active microwave sensors typically in the Ku or Ka bands under all atmospheric conditions. Once the data are quality controlled and sensor biases are removed, H s errors are comparable to buoy measurements  (Zieger et al. , 2009; Sepulveda et al. , 2015). We use the merged and calibrated data set of  Queffeulou and Croize-Fillon (2016). The reprocessed wave measurements from European Remote Sensing satellites 1 and 2 (ERS1, ERS2), Environmental Satellite (ENVISAT), Geosat Follow-On, CRYOSAT2, and Altika SARAL are used throughout this study.
Our results are calculated using the 3 h model output for the hindcast duration. H s percentiles are calculated from the ice-free statistics and the matching H s index is used to identify corresponding wave periods and directions. A ±0. 2 m bounds of the associated H s index is used to average the wave periods and directions. This approach gives a more accurate physical description of the events  (Anderson et al. , 2015). We compute trends using Sen's method and test for statistical significance with the Mann-Kendall test. This method is a non-parametric technique and a robust way of computing trends since it can handle missing data and is less influenced by outliers  (Mann, 1945; Kendall, 1975; Sen, 1968). We account for the seasons using the adaptation of  Hirsch et al. (1982)  and used in wave climatology studies by  Wang and Swail (2001) ,  Young et al.
Wave hindcast validation Before the wave climate is assessed we validate the hindcast using the merged altimeter data set for 1992-2014. Altimeter-model co-locations are found using the nearest neighbor within 6 km and 30 min. A running mean of 5 points smooths the satellite tracks to make the spatial and temporal scales comparable. The top 4 panels of Fig. 3  show four complementary H s statistics computed in 25 km bins. There is minimal bias for the majority of the domain; however, some errors exist. In the Baffin Bay and the region north of Svalbard there is an underestimation of 5 and 10 %, respectively. Otherwise spurious negative biases are located near the coasts. In the Beaufort, Chukchi, and Laptev seas the model overestimates the wave heights by 5-10 %. The root-mean-square errors (RMSEs) are commonly less than 0.
Data availability The CFSR and ERAI reanalysis data are publicly available from rda. ucar. edu and the IFREMER/CERSAT sea ice concentration is available from ftp. ifremer. fr/ifremer/cersat/. The altimeter data are available from ftp://ftp. ifremer. fr/ ifremer/cersat/products/swath/altimeters/waves/. Wave hindcast data will be made available at ftp://ftp. ifremer. fr/ifremer/ ww3/HINDCAST/  (Stopa, 2015). Table  A1  displays error H s metric with the NDBC buoys. The CFSR hindcast overestimates the H s by at least 5 % at all locations while the ERAI hindcast underestimates by 3 % (except WMO48213). The RMSEs are commonly 0. 25 m, with the ERAI hindcast always having a better agreement. The scatter indices and correlation coefficients for the ERAI and CFSR wave hindcasts are similar at each buoy.
Crossovers are depleted at hot spots with high genetic diversity The locations of crossovers in male B6 × CAST, B6 × PWK, and PWK × CAST hybrids were obtained from the Collaborative Cross  (Liu et al. 2014). PWK data were used as a proxy for the closely related PWD strain. We compared with DSB hot spots in the same genetic background and retained only crossovers that intersected a single DSB hot spot. This allowed us to infer the DSB that each crossover originated from and yielded 141, 221, and 215 crossovers in the B6 × CAST, B6 × PWK, and PWK × CAST F1s respectively. The divergence at DSB hot spots was calculated by counting the number of nucleotides affected by SNPs and indels in a defined window around the hot spot center. Each indel incremented this count by the size of the indel, while each SNP incremented the count by 1.