dataesr/scientific-paragraphs-categorization huggingface dataset:
ATAC-seq data analysis Bcl files were converted and demultiplexed to fastq using the bcl2fastq program. STAR  74  was used to index the human reference genome (hg19), obtained from UCSC, and align the resulting fastq files. The resulting bam files with the mapped reads were then converted to tag directories with subsequent peaks calling using the HOMER suit of programs  48. HOMER was also employed for counting the reads in the identified peak regions. The raw tag counts from the peaks were then imported to R/Bioconductor and differential peak analysis was performed using the edgeR package and its general linear models pipeline. Peaks with an FDR adjusted p value under 0. 05 were termed significant. Plotting was done in R using packages Complex heatmap, ggplot2 and ggbeeswarm. RepeatMasker table downloaded from UCSC (http://hgdownload.
soe. ucsc. edu/goldenPath/hg19/database/rmsk. txt. gz) was converted to BED format and then intersected with the ATAC-seq peaks using the intersectBed from BEDTools  46  to determine the peaks overlapped with ERVL-MaLR elements. ATAC-seq data of human early embryo were obtained from GSE101571  30  , and scores around the ATAC-seq peaks were calculated with computeMatrix and visualized with plotHeatmap from deepTools  47. All the scripts and command line options can be provided upon request.
De novo motif enrichment analysis of the DUX4-induced TFEs. Top: the most significantly enriched motif (P = 1e-961). Bottom: the best-matched known motif (DUX4 ChIP-seq of myoblasts: GSE75791 29 ; matched score = 0. 92). (i) Enrichment analysis of the DUX4-induced TFEs with publicly available ChIP-seq datasets. A total of 7,216 ChIP-seq data for transcription factors are shown. ChIP-seq data for DUX4 are shown in red. Dots on the left side of the dashed line are underrepresented, whereas dots on the right side are overrepresented.
( h ) h De novo motif enrichment analysis of the DUX4-induced TFEs. Top: the most significantly enriched motif (P = 1e-961). Bottom: the best-matched known motif (DUX4 ChIP-seq of myoblasts: GSE75791 29 ; matched score = 0. 92). (i) Enrichment analysis of the DUX4-induced TFEs with publicly available ChIP-seq datasets. A total of 7,216 ChIP-seq data for transcription factors are shown. ChIP-seq data for DUX4 are shown in red. Dots on the left side of the dashed line are underrepresented, whereas dots on the right side are overrepresented.
Globe -LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. the primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales.
Field sampling is labour-intensive and sampling sites must be carefully selected to represent spatial variation in LFMC and vegetation types. Sampling must also be repeated over time to capture temporal variation in LFMC. Consequently, the compilation of a database capturing broad-scale spatial and temporal variability in LFMC is not feasible with the resources of a single organization or research group. Remote sensing data provide the opportunity to predict LFMC over large areas at fine spatial and temporal resolutions, but these data also require field samples for calibration and validation  1. Given the large cost of collecting field measurements of LFMC over large areas or long time periods, an international effort to compile and share existing field observations in a global database would help overcome a key constraint for the improvement and validation of LFMC remote sensing methods.
Individual universities, research centres and government departments have started to organize and share their time series of field-sampled LFMC data. For example, the U. S. National Fuel Moisture Database 8 (NFMD, http:// www. wfas. net/nfmd/public/index. php) is a web-based query system that enables users to view live-and deadfuel moisture data. Chuvieco 9 made available a database (FMC_UAH v1. 1, http://www. geogra. uah. es/emilio/ FMC_UAH. html) composed of 880 LFMC samples taken at different campaigns from 1996 to 2010 in Spain. Since 1996, the French National Forest Service (ONF) has been sampling and freely sharing weekly LFMC (www. reseauhydrique. dpfm. fr) on 35 geolocalized sites, recently quality-checked and made available by Duché et al. 10, 11. However, while a large number of LFMC datasets have been published in the refereed literature, much of the source data has not previously been made available to the research community.
We present Globe-LFMC  12  , the most comprehensive global database of in situ destructive sampling measurements of LFMC. Globe-LFMC is a compilation of 161,717 field measurements carried out at 1,383 sampling sites in 11 countries from 1977 to 2018 (Fig. 1 ). The database is properly documented, georeferenced and publicly accessible. When available, each record has an accompanying reference. We have made all names of sampling sites and species names consistent. We have also removed duplicates and corrected inconsistencies in the LFMC   19  ). A majority of samples were collected in the Western US, France, Spain and Australia. Fig. 2  The linkage between Globe-LFMC spreadsheets. data. Additionally, the database reports on the protocol used to obtain each LFMC value. Finally, we also used remote sensing to assess the heterogeneity of vegetation greenness surrounding site coordinates, since highly heterogeneous areas within a specific satellite footprint may not be suitable for the calibration or validation of remote sensing products).
This database will lead to further advances in modeling and monitoring of spatial and temporal variation in LFMC. It should also allow evaluation of LFMC estimation methods, providing guidance for end-users in determining which LFMC estimation methods best fit their specific application. The database will also assist investigation of spatial variability in LFMC across the plant, local, and regional scales, and allow improved sampling strategies to capture spatial and temporal variation. The database can also be used to calibrate dynamic global vegetation models, eco-physiological models of plant drying as well as understanding the environmental and physiological drivers of LFMC. Finally, the database may be useful for exploring LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behaviour and overall vegetation health.
Reference Citation or link to original LFMC data sets or to relevant publications that use the data or describe the site (in cases where the collection of data in an area proceeded after the publication cited, the record in Globe-LFMC was still linked to the article related to the older sampling campaign, in order to provide a description of the site). Name of picture file Name of the photograph showing the sampling site. Table  2. Column description of the LFMC data spreadsheet.
Methods Globe-LFMC unifies existing LFMC data created and provided by researchers and agencies in different countries (Fig. 1 ). All of the data presented in the database were collected by in situ destructive sampling of leaf material or, occasionally, small twigs (<0. 6 cm). After the mass of fresh samples was determined, samples were dried in an oven until the water was evaporated, and then the sample was reweighed to determine dry mass. LFMC is typically calculated as the percentage of water mass with respect to dry mass, and can thus be over 100%. The sampling methods for the different data sources were slightly different in terms of the equipment used to collect the samples, the drying temperature and time and other protocols for data acquisition or processing. Globe-LFMC summarizes the sampling methods used via a unique code (see Data Records paragraph), and the detailed description of the methodology can be found in the citation of each code included in the database.
Overall, only the LFMC from leaves or small terminal twigs (<0. 6 cm) was considered and added to the database. Occasionally information about other vegetation components was recorded in the source data but this information was omitted in Globe-LFMC because leaves are generally the dominant component when viewing vegetation from above and thus contribute the most to the spectral signal observed by an airborne/spaceborne sensor. Moreover, any quality control flags in the source data indicating low-quality data led to the omission of the corresponding LFMC values in Globe-LFMC.
Observations Further comments and information regarding the protocol used. LFMC values from samples corresponding to the same date, species and site were recorded as a mean value. This approach allowed us to maintain consistent information on every single species sampled at the same plot. However, in some instances, the sampler collected and weighed different species together in the same sample. In those situations, the "Species collected" database field contains a list of species instead of a single species. Sometimes the species were reported with their common names or with typos in the original datasets, in which case the correct genus and species was substituted. There were some entries where the same site name was used for more than one set of geographic coordinates. In order to have a single LFMC value for each species-date-site combination, the names were modified by adding an identifier (e.
g. an increasing number or the state abbreviation). Conversely, we found some entries where two or more different site names corresponded to identical geographic. Those names were unified creating a new name (e. g. from "A" and "B" to "A-B", where A and B are two different names, or from "C1" and "C2" to "C", where C corresponds to the word that was in common in the C1 and C2 names). Plots with missing geographic coordinates were not added to the final database. Most of the information provided came from the original datasets, but a few columns were added to the database to provide additional insight into site characteristics. A column was added for "Land Cover" that provides information on the land cover class at the sample site obtained from the 2015 ESA Climate Change global land cover map at 300 m spatial resolution for the year 2015 (http://maps.
Monthly mean NDVI maps were created using USGS Landsat 8 Surface Reflectance Tier 1 data and masking the pixels marked as cloud, cloud shadows, or snow, for each month for 2015 (the same year of the ESA land cover map used for the characterization of the land cover type of each site). For each of the 12 monthly maps, the standard deviation and the mean were computed within the 500 m × 500 m window. If 20% or more of NDVI values within the window were missing due to cloud and snow masking, no NDVI value was reported for that month. Consequently, every site was assigned 12 NDVI standard deviation values (one for each month) and 12 NDVI mean values. Globe-LFMC contains the minimum and maximum Standard Deviation and the Coefficient of Variation of NDVI values of every site. Finally, we also added information on slope and altitude for some sampling plots.
Description of google earth engine code. Google Earth Engine 14 was used to compute the NDVI statistics added to Globe-LFMC. The input of the program is a point shapefile ("samplePlotsShapefile", extensions. cpg,. dbf,. prj,. shp,. shx) representing the location of each Globe-LFMC site. This shapefile is available as additional data in figshare  12  (see Code Availability). To run this GEE code the shapefile needs to be uploaded into the GEE Assets and, then, imported into the Code Editor with the name "plots" (without quotation marks). The outputs of the program are 12 ". csv" files, each corresponding to a month of the year 2015. Every file contains the following statistics for the 500 × 500 m 2 buffers around the coordinates of the Globe-LFMC site: NDVI SD, NDVI mean, the count of total pixels and the unmasked pixels.
Data Records The compiled data are available in a single database in Excel format with three different interrelated spreadsheets; "Contact", "LFMCdata" and "Protocol 12 " (Fig. 2 ). A description of the fields in each spreadsheet can be found in Tables  1, 2  and 3. Each data record represents the LFMC measurement taken at a sampling site (Sitename) at a specific time and has a unique record identification code: C(contact_id)_(Sitename _id)_(record_id). These Fig. 3  Boxplots representing the seasonal LFMC of some of the species with more observations. The number of observations used to compute each box has been added. The range of dates used to identify each season was defined using the astronomical Universal Time-based equinoxes and solstices. For the countries of the northern hemisphere, Spring was considered to be between 20  details allow users to select and download discrete datasets for their area of interest, and to identify the contact person for each data entry.
technical Validation The database represents a range of countries and land cover types containing LFMC values that range from 0. 21-549% (Table  4 ). A majority of samples were collected in the Western US, due to extensive government sampling programs for assessing wildfire danger, with some time series stretching back decades. Large numbers of samples were also collected in France, Spain and Australia (Table  4 ). The land cover type with the largest number of observations and sites is "Tree cover, needle-leaved, evergreen, closed to open (>15%)" followed by "Shrubland", "Grassland" and "Cropland-rainfed" (Table  5 ). Data in the database have been checked for possible replications and errors. We validated the data by checking their consistency with expected LFMC ranges, noting that it is out of the scope of this paper to provide detail LFMC trend analysis as this will be the objective of future work.
Usage Notes Users of the database are encouraged to look at the available photos of the sites, whose names can be found in the "LFMC data" spreadsheet. The photos are contained in the zip folder named "photos of sites". If the database is to be used for remote sensing products calibration or validation, fields for minimum and maximum "NDVI SD" and "NDVI CV" are recommended to be explored for a selection of the most homogenous sites. An extra database in Excel format ("References&Changes_LFMC. xlsx", at figshare)  12  with two spreadsheets "References" and "Changes to USA National FM db" contain information on references, copyright notices and list the changes to the original datasets. We provide this information in case a researcher would like to compare the values shown here to the original databases.
Code availability The Google Earth Engine (GEE)  14  code and the shapefile "samplePlotsShapefile" (extensions. cpg,. dbf,. prj,. shp,. shx), used for computing the NDVI statistics are part of the data and files uploaded together with Globe-LFMC into figshare  12. This code can only be run if the user has access to a Google account and to GEE.
To study key processes of the water cycle, two special observation periods (SOPs) of the Hydrological cycle in the Mediterranean experiment (HyMeX) took place during autumn 2012 and winter 2013. The first SOP aimed to study high precipitation systems and flash flooding in the Mediterranean area. The AROME-WMED (western Mediterranean) model (Fourrié et al. , 2015)  is a dedicated version of the mesoscale Numerical Weather Prediction (NWP) AROME-France model, which covers the western Mediterranean basin providing the HyMeX operational center with daily real-time analyses and forecasts. These products allowed for adequate decision-making for the field campaign observation deployment and the instrument operation. Shortly after the end of the campaign, a first reanalysis with more observations was performed with the first SOP operational software.
An ensuing comprehensive second reanalysis of the first SOP, which included field research observations (not assimilated in real time) and some reprocessed observation datasets, was made with AROME-WMED. Moreover, a more recent version of the AROME model was used with updated background error statistics for the assimilation process. This paper depicts the main differences between the realtime version and the benefits brought by HyMeX reanalyses with AROME-WMED. The first reanalysis used 9 % additional data and the second one 24 % more compared to the real-time version. The second reanalysis is found to be closer to observations than the previous AROME-WMED analyses. The second reanalysis forecast errors of surface parameters are reduced up to the 18 and 24 h forecast range. In the middle and upper troposphere, fields are also improved up to the 48 h forecast range when compared to radiosondes.
Introduction The HYdrological cycle in the Mediterranean EXperiment (HyMeX;  Drobinski et al. , 2014 ) is a 10-year scientific program aiming at a better understanding and quantification of the hydrological cycle and related processes in the Mediterranean region. Emphasis is on high-impact weather events, interannual to decennial variability of the Mediterranean coupled system, and associated trends in the context of global climate change. The first special observing period took place in autumn 2012 (5 September to 6 November 2012) to study heavy precipitation and flash flooding events  (Ducrocq et al. , 2014). An AROME (Application of Research to Operations at Mesoscale;  Seity et al. , 2011)  model version dedicated to the HyMeX program, the AROME-WMED (western Mediterranean) model  (Fourrié et al.
, 2003)  and the Mesoscale Alpine Programme (MAP;  Keil and Cardinali, 2004)  with a view to providing a new reference description for process studies. In the framework of the Innovative Observing and Data Assimilation Systems for severe weather events in the Mediterranean project, it was decided to perform reanalyses of the HyMeX special observation period to benefit from additional research observations, as well as from advances in assimilation algorithms and modeling. Shortly after the HyMeX campaign, a first reanalysis (RE-ANA1), which did not include any new data processing, was performed to provide scientists with a unified dataset for process studies. The real-time AROME-WMED version was indeed upgraded during the field campaign on 25 September 2012 at 06:00 UTC. More recently, a second reanalysis of the HyMeX special observation period (REANA2) was undertaken to take advantage of observations deployed during the field campaign not included in SOP1 or REANA1, as well as enhanced reprocessed datasets.
REANA2 also benefited from the latest model updates. The aim of this paper is to review the main characteristics of the AROME-WMED reanalysis versions in terms of data assimilation and forecast and to compare them with their real-time counterpart. The outline of the paper is as follows. Section 2 compares both configurations of the AROME-WMED reanalysis and the real-time versions. The different datasets assimilated in the reanalyses are specified in Sect. 3. Section 4 evaluates the assimilation and forecast with respect to various observations. The qualitative and quantitative precipitation evaluation of the three AROME-WMED versions for the Intensive Observation Period (IOP8) case study is discussed in Sect. 5. Conclusions are found in Sect. 6.
, 2000)  and a mass flux scheme  (Pergaud et al. , 2009)  to account for dry thermal and shallow convection. There is no deep convection parameterization. A specific algorithm named CANOPY  (Masson and Seity, 2009)  diagnoses the 2 m temperature, 2 m humidity, and 10 m wind at every time step in the surface scheme (SURFEX;  Masson et al. , 2013). The AROME-WMED domain (34 • N, 11 • W; 48 • N, 20 • E) ranges from Portugal to Italy and from North Africa to France (Fig. 1 ). It was designed to study high precipitation events that occur over the northwestern Mediterranean Table  1  lists the main differences of configuration in the model. The same method as in AROME-France was used to set up the surface characteristics for the SUR-FEX scheme. Physiographic data are initialized over the AROME-WMED domain using the so-called ECOCLIMAP database at 1 km resolution  (Masson et al.
, 2003). The topography is extracted from the Global 30 Arc-Second Elevation Data Set (GTOPO30; https://www. usgs. gov/media/ files/gtopo30-readme, last access: 3 July 2019) for the realtime version and the first reanalysis. In the second reanalysis, the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010;  Danielson and Gesch, 2011)  database was used. A mean difference of -21 m was found between the orography interpolated onto the AROMEWMED grid from GMTED2010 used in the REANA2 and the one interpolated from GTOPO30 used in the REANA2 and SOP1 versions (Fig. 1 ). Lateral boundary conditions are provided by the Météo-France global NWP ARPEGE system  (Courtier et al. , 1991). For REANA2, ARPEGE forecasts benefit from a maximum of assimilated data using longer cutoff analyses than for RE-ANA1 and SOP1.
Observations common to all AROME-WMED versions Both the REANA1 and REANA2 reanalyses used all available data with no time constraint (cutoff), contrary to the SOP1 (real-time) version. These observations come from radiosondes, including mobile sites along the French Mediterranean coast, surface stations and buoys, aircraft, and wind profilers. Satellite data are dominant in the analysis, contributing to more than 50 % of the assimilated data flow, since a large part of the domain is over the sea. Satellite data comprise infrared and microwave radiances from polar-orbiting satellites, radiances from SEVIRI onboard the Meteosat Second Generation (MSG), surface wind from scatterometers over the Mediterranean Sea, and atmospheric motion vectors. GNSS (global navigation satellite system) zenith total delay (ZTD) observations from the EUMETNET EIG GNSS water vapor program (E-GVAP) network are assimilated as well.
Observations specific to REANA2 In addition, new datasets and reprocessed observations were assimilated in REANA2. Table  2  summarizes the main differences in terms of assimilated observations between the two reanalyses. The GNSS zenithal total delays from the reprocessed dataset available in the HyMeX database  (Bock et al. , 2016)  have been used. The methodology for their assimilation is described in  Mahfouf et al. (2015). All available GNSS data were reprocessed homogeneously with a single software program, more precise satellite orbits and clocks, and additional sites taken into account (e. g. , Sardinia). This led to better coverage as shown in Fig. 4 , especially over France, the Iberian Peninsula, and Italy. Furthermore, an updated static bias correction for each couple (GNSS station, analysis center) was computed for the REANA2 version.
Data from several Spanish Doppler radars (Valencia, Barcelona, Murcia, Almeria, and Palma) were also used in the second reanalysis after careful quality control. Wind profiler data were also carefully checked in order to remove spurious signals  (Saïd et al. , 2016). Humidity data retrieved from groundbased and airborne lidars were processed. Two ground-based research lidars were processed: one located in Candillargues  (BASIL instrument; Di Girolamo et al. , 2016)  and the other on Menorca, an island in Spain (WALI instrument;  Chazette et al. , 2016). These data were smoothed through an interpolation at a 200 m vertical resolution and outliers were removed. The lidar Leandre II data (temperature and wind) from 22 ATR flights were also assimilated according to the method described in  Bielli et al.