dataesr/scientific-paragraphs-categorization huggingface dataset:
(2) The user registers for the first time. He/she must go to http://mistrals. sedoo. fr/User-Account-Creation/, not checking the box corresponding to "Simple registration (name, affiliation, country) for direct access to public data only", and follow the registration. Additionally a bundling service was performed to facilitate the use of the data. The bundled data include the most commonly used data in hydrometeorological and hydrological studies. The bundled data have the advantage of gathering data in ASCII and Cartesian format in a single coordinate system and in the same time zone (UTC). The bundled data are selected for the spatial and temporal windows presented in the paper (see the extent of Figs. 3  and 6 ) since some individual datasets have different extents. For the remaining datasets, not included in the bundled data, the effort to prepare the data was judged too laborious and their potential more restricted.
Such datasets remain accessible individually even though they are not necessarily in the same format and with the same extent (polar vs. Cartesian and coordinate system). The bundled data are organized in two independent ways: (i) for the public datasets which refer to Tables  5  and 6 , the "zip1" file (602 MB) is accessible directly (without registration) clicking on the purple icon of the "data access" section of the page http:// mistrals. sedoo. fr/MISTRALS/?editDatsId=1438; (ii) for the additional datasets subject to the HyMeX data policy, the "zip2" file (4 MB) is accessible by clicking on the blue icon of the "data access" section of the page http://mistrals. sedoo. fr/MISTRALS/?editDatsId=1438. Alternatively, there are other ways to access and visualize the data: the SEVnOL system, maintained by IGE, on the OHMCV website (http://www.
ohmcv. fr) and the BDOH database (https://bdoh. irstea. fr/OHM-CV/) maintained by IRSTEA and managed by the data producers (IRSTEA, IGE). SEVnOL and BDOH are complementary tools to the bundling service proposed in this study (through the release of the "zip1" and "zip2" files). BDOH was developed for the management of long-term time series and enables the following features: visualization of data, downloading of data, interpolation of time steps for export, import and export to multiple formats, and automatic calculation of derived time series. SEVnOL is a web interface developed to view and extract data, metadata and products in several formats (XML, CSV, NetCDF) over a user-defined spatial and temporal window  (Boudevillain et al. , 2011).
Conclusion A high space-time resolution dataset linking hydrometeorological forcing and hydro-sedimentary response in a mesoscale catchment is presented. The Auzon catchment (116 km 2 ), a tributary of the Ardèche River, is subject to precipitating systems of Mediterranean origin, which can result in significant rainfall amount. The data presented cover a period of 4 years  (2011) (2012) (2013) (2014) , including the HyMeX-SOP1 field campaign  (Ducrocq et al. , 2014)  and the ANR Flood-Scale project  (Braud et al. , 2014) , which aims at improving the understanding of processes triggering flash floods. The multi-scale observation system presented is part of the OHMCV  (Boudevillain et al. , 2011). The operational and research networks provide high space-time resolution data (< 1 km 2 , 5 min) for studying the microphysics of precipitating systems and producing QPE particularly adapted to finescale hydrological studies.
The measurement of the other meteorological variables relies almost exclusively on the operational network (1 h time resolution). Validation data are both spatially distributed and multi-scale. They include point measurements of soil moisture (fixed sensors in continuous mode and mobile sensors during rain events), runoff and erosion measurements on hillslope, water level measurements in the intermittent hydrographic network of headwater catchments (11 points of measurement) and hydrometric measurements (discharge, water conductivity and temperature) at the outlet of three nested catchments (3. 4, 44 and 116 km 2 ). Discharge measurements were made at high water levels during on-alert campaigns to establish stage-discharge relationships. It is hoped that using this dataset will lead to advances in understanding hydrological processes leading to flash floods and improving distributed hydrological models.
the Ardèche soil database at 1 : 100 000 IRSTEA Lyon Public Maximum soil water storage for each SCU Vector 2015 Processed from the Ardèche soil database at 1 : 100 000 IRSTEA Lyon Public Soil texture of superficial layer for each SCU Vector 2015 Processed from the Ardèche soil database at 1 : 100 000 IRSTEA Lyon Public Soil stone content for each SCU Vector 2015 Processed from the Ardèche soil database at 1 : 100 000 digital terrain model. b SCU: soil cartographic unit. c Not released in this study.
Acknowledgements. The  FloodScale  project is funded by the  French National Research Agency (ANR)  under contract no. ANR 2011 BS56 027 , which contributes to the HyMeX programme. It also benefits from funding by the  MISTRALS/HyMeX programme  (http://www. mistrals-home. org). OHMCV is supported by the  Institut National des Sciences de l'Univers (INSU/CNRS) , the  French Ministry for Education and Research, the Environment Research Cluster of the Rhône-Alpes Region , the  Observatoire des Sciences de l'Univers de Grenoble (OSUG)  and the  SOERE Réseau des Bassins Versants (Alliance Allenvi). The development of the BDOH database was supported by  Irstea  internal funding and the  Rhône Sediment Observatory (OSR)  project, partly funded by the  Plan Rhône. The equipment of the erosion plots and part of the Gazel hydrometric station was funded by the  Rhône-Alpes Region.
The Sontek-IQ Plus instrument was funded by the  French National Research Agency (ANR)  under contract no. ANR-12-JS06-0006  ( SCAF  project). The authors thank the providers of operational data:  Météo-France  and the  SPC Grand Delta. The contract of  Simon Gérard  was funded by the  Institut National des Sciences de l'Univers (INSU/CNRS). The PhD thesis of  Annette Wijbrans  was funded by  Rhône-Alpes Region. We warmly thank colleagues of  IGE  (especially  Romain Biron  who participated at the beginning of the project), including students and technical staff (with special thoughts to  Matthieu Le Gall ), who helped in installing and maintaining the observation system. We also thank  Isabella Zin  and  Jérémy Chardon  for providing the analogue rainfall forecasting. IGE is part of  Labex OSUG@2020  ( ANR10 LABX56 ), which funded the contract of  Martin Calianno , who also assisted with the first installations.
The HyMeX database teams (ESPRI/IPSL and  SEDOO/Observatoire Midi-Pyrénées)  and the team of the OSUG data centre helped in accessing the data and attributing DOIs to the individual datasets. In addition, the authors acknowledge the  EPLEFPA Olivier de Serres ; the municipalities of  Lavilledieu, Lussas, Mirabel, Saint-Germain, Saint-Etienne de Fontbellon, and the middle school of Villeneuve de Berg ; and the local landowners and neighbours for hosting the experiments. Timothy H. Raupach  acknowledges the support from the  Swiss National Science Foundation. Edited by: K. Elger Reviewed by: two anonymous referees.
During preliminary campaigns of cosmic ray observation, a few meteor events have been already detected, confirming the expectations. The paper is organized as follows. Section 2 and 3 will briefly report on the concept of the JEM-EUSO and Mini-EUSO detectors and their expected performance for meteor and nuclearite detection, the latter being exotic particles which have been suggested by some theoretical investigations to address the dark matter issue  (De Rujula and Glashow, 1984). Section 4 will summarize the ongoing efforts to implement into ESAF  (Berat et al. , 2010) , the software package employed by the JEM-EUSO collaboration, the basic properties of meteor phenomena and the necessary procedures for their detection with the JEM-EUSO and Mini-EUSO instruments. Section 5 shows the first examples of meteor tracks detected by means of the JEM-EUSO observational approach, using the EUSO-TA facility in Utah.
The electronics are configured in three levels corresponding to the structure of the focal surface detector: front-end electronics at EC level, PDM electronics common to 9 EC units, and focal surface electronics to control 137 units of PDMs. The anode signals from the MAPMT are digitized at time intervals (named Gate Time Units, GTU) of 2. 5 |js and held in ring memory for an event to trigger the onboard software. After triggering, the data are sent to control boards. JEM-EUSO uses a hierarchical trigger method to reduce the data rate of ~10 GB/s down to 297 kbps, required for transmission from the ISS to the ground operation center. In this way, the pulses produced by incident UV photons are read and recorded by the on-board electronics. Whenever a signal pattern is detected, it triggers the start of a sequence of recording operations which involve a selected pixel region on the detector.
According to current plans, two visible cameras should also be installed as ancillary instruments to complement the UV detections performed by the Mini-EUSO focal assembly. The Mini-EUSO instrument will be inserted in a specific container, a mechanical box providing also all the needed interfaces to the transparent, nadir looking UV window of the Zvezda Russian module aboard the ISS. The FOV of Mini-EUSO will be about ±20°, corresponding to a radius at ground of ~ 150 km, with ~0. 8° per pixel. The trigger system and data acquisition is organized in different time scales. For meteor observation purposes, Mini-EUSO will continuously record data with 40 ms time resolution. Data are saved on disks aboard the ISS, and periodically brought to the ground by astronauts for data reduction. Meteor detection and tracking will be performed on ground after data recovery.
Implementation of meteor simulations in ESAF The performance of JEM-EUSO and Mini-EUSO has been so far evaluated using a simple numerical simulator of meteor phenomena, as described in Section 2. Currently, there is an on-going effort to include this already developed software in the ESAF (EUSO Simulation and Analysis Software) package. The reason is that ESAF is the official software tool to perform simulations of extended air shower (EAS) development of cosmic ray, photon production and transport through the atmosphere, and detector response for optics and electronics. Moreover, ESAF includes also algorithms and tools for the reconstruction of the properties of air showers produced by extremely energetic cosmic rays. Originally developed for the EUSO-ESA mission, all the necessary steps have been taken in recent times to implement in ESAF all the planned JEM-EUSO, Mini-EUSO and EUSO-TA instrumental configurations, in order to assess the full range of expected performances for cosmic ray observation  (Bertaina et al.
7c ) with a field of view of 11° x 11° (FOV of ~0. 2° per pixel). The EUSO-TA apparatus is triggered externally by the TA experiment and images cosmic ray tracks. Fig. 7d  shows a typical example. The signal lasts only 1 GTU, as the cosmic ray shower is detected very close to the apparatus (less than 3 km). For this reason EUSO-TA can only record packets of 128 GTUs (for a total of 320 |js) of data with a maximum data rate of 20 Hz. In reality, as the typical trigger rate of TA is ~ 3 Hz, only a few snapshots of any meteor track can be detected. In spite of this difficulty, so far 5 meteor events have been detected. Fig. 8  shows an example of such events. Plot (a) in this figure shows the integrated number of counts in each pixel. A 3a threshold has been applied on each pixel to exclude the signal not due to the meteor.
An exciting development, though limited to sufficiently bright  meteors, is the possibility to exploit the persistence of the meteor train and the movement of the ISS to obtain a 3D reconstruction of the meteor trajectory and velocity, making it possible to compute the original heliocentric orbit of the meteoroid, a very important result for solar system studies. Our preliminary analysis concerning the possible detection of nuclearites indicates that JEM-EUSO will be sensitive to nuclearites having masses higher than a few 10 22 GeV/c 2. In addition, after a JEM-EUSO data acquisition time of only 24 h, it will be able to provide limits on the possible nuclearite flux one order of magnitude lower than the limits reached by the experiments carried out so far. Prior to JEM-EUSO, the Mini-EUSO pilot mission is expected to fly aboard the ISS on 2017-2018.
It will prove the JEM-EUSO observa-tional principle and by means of detailed offline data analysis it will allow us to detect meteors having magnitude as faint as absolute magnitude M < + 5. Mini-EUSO will be sensitive to nuclearites with mass higher than a few 10 24 GeV/c 2. After one month of operations it could reach a sensitivity of about 2 orders of magnitude better than what has been obtained so far by means of ground experiments. A ground-based pathfinder mission of JEM-EUSO, named EUSO-TA, is in operation since 2013 in Utah. Despite the fact that the detector is specifically designed to be sensitive to cosmic ray events, a few meteors have been observed, proving on ground the observational principle of JEM-EUSO. There are plans to equip in near future EUSO-TA electronics with a meteor trigger in order to observe more efficiently this category of events.
Acknowledgements We thank two anonymous Referees for their detailed and exhaustive reviews, which led to a substantial improvement of this work. We also thank  Dr. P. Jenniskens  for his kind help in assessing the predictable intensity of meteor light observed at UV wavelengths. This work was partially supported by  Basic Science Interdisciplinary Research Projects of RIKEN  and  JSPS KAKENHI  Grant ( 22340063 ,  23340081 , and  24244042 ), by the  Italian Ministry of Foreign Affairs and International Cooperation , by the  'Helmholtz Alliance for Astroparticle Physics HAP ' funded by the  Initiative and Networking Fund of the Helmholtz Association, Germany , and by  Slovak Academy of Sciences  MVTS JEM-EUSO as well as  VEGA  grant agency project  2/ 0076/13. Russia is supported by the  Russian Foundation for Basic Research  Grant  No 13-02-12175 -ofi-m.
Flow cytometry Cells, preincubated with mAb 2. 4G2 (anti-CD16/32) to block Fc receptors were labeled with CD4 AlexaFluor 700 (GK 1. 5, eBioscience). Cytokine-detection was performed by intracellular cytokine staining with anti-IFN-γ Alexa Fluor 488 (XMG1. 2, eBiosciences) and anti-IL-10 BV421 (JE55-16E3, Biolegend). Cells (1. 5 × 10 5 cells/well) were stimulated at 37°C with 10 ng/ml phorbol myristate acetate (PMA, Sigma), 1 µg/ml ionomycin (Sigma) for 4 h, and 5 µg/ml Brefeldin (Sigma) for 2 h and permeabilized using Foxp3/Transcription Factor Staining Buffer Set (eBiosciences). Data were acquired using a LSR II cytometer (BD Biosciences).
cFse labeling Naive CD4 + T cells, suspended in 5-µM CFSE staining buffer (Molecular Probes) and incubated at 37°C for 10 min, were cultured as described above. After 5 days of culture, cell division was determined by measuring CFSE fluorescence in total cells. Cell viability was determined by measuring Fixable viability Stain 620 (FVS620, BD Bioscience). Data were analyzed using the FlowJo V10 software.
Protein isolation and analysis Total cell lysates were prepared in RIPA buffer (50 mM Tris-HCl pH 7. 4, 2 mM EDTA pH8, 150 mM NaCl, 0. 5% Na-deoxycholate, 0. 1%SDS, 1% Non-idet P40) supplemented with protease inhibitors (Mini protease inhibitor, Roche). Samples were separated on a 8% SDS-polyacrylamide gel and transferred to nitrocellulose. Rat anti-mouse Blimp1 (5E7, Santa Cruz) at 1/200 dilution, rabbit anti-mouse β-Actin (N-21, Santa Cruz) at 1/600, Goat anti-rat or rabbit IgG-HRP 1/10000 were used and visualized by chemiluminescence (ECL, Amersham Pharmacia Biotech); relative density of Blimp1 expression analyzed using the ImageJ software.
statistical analysis The data were presented as mean ± SEM of at least three independent experiments (n ≥ 3). The statistical analysis of the data was performed using Student's t-test for single comparisons and by ANOVA followed by Bonferroni's test, and two-way ANOVA test for multiple comparisons with the GraphPad software (La Jolla, CA, USA). In all cases a p-value of 0. 05 or less was considered as statistically significant.
Study design This multicenter, cross-sectional web-based evaluation included 37 endocrinologists from Spain and Portugal, with an average of 17 years of experience in diabetes. Participants were asked to analyze 4 cases, obtaining a maximum of 145 evaluations; we esteemed it would provide a reasonable statistical power. The evaluation was organized in three parts: a preliminary questionnaire on eDetecta initial experiences (having used eDetecta in usual clinical practice with at least four patients), an evaluation of reports of real-life data downloads, and a final questionnaire on current barriers related to data analysis in clinical practice.
Data collection Total time spent per participant to analyze every report consisted of two separate times: (1) time needed to read the report and (2) time needed to evaluate the presence or absence of patterns and answer the corresponding questions. Time spent by eDetecta on the automated analysis for each one of the four reports was also registered. All times were measured in minutes. Participant responses on the presence of patterns were compared with automated detected patterns by eDetecta. The questionnaire on current barriers related to data analysis in clinical practice included a total number of 30 questions, most of which were based on a 5-point score Likert-type scale (i. e. , 1 is ''not reliable at all'' and 5 is ''very reliable'').
DIV51 ) and analyzed by qRT-PCR and next generation sequencing. qRT-PCR analyses show statistically significant knockdown of HTT gene expression (71% ± 7% [SD]; p < 0. 01; Figure  4C ). This result was confirmed by RNA sequencing (RNA-seq) data showing a similar knockdown effect of shHTT6 (71%; p = 0. 0138; Figure  4D ) on HTT transcript NM_002111. TaqMan qRT-PCR, based on SNP rs362331,  49, 73  which is heterozygous in the HD-iPSC line, showed that the allelic ratio was not altered by shHTT6 treatment (Fig-  ure 4E ). 74 These results indicate that the pCCL-shHTT6 targets both the mutant and the WT HTT alleles in equal proportion. RNA-seq expression of classical neuronal markers revealed no significant effect of shHTT6 on the neuronal phenotype of the culture. The shHTT6/shLUC ratio remained close to 1.
Consistent with the silencing data described above, pCCL-shHTT6 (t test, p < 0. 01) and SIN-shHTT6 (Mann-Whitney U test, p = 0. 051) efficiently reduced luciferase expression with preferential loading of the guide strand (Figure  6A ). We extracted and sequenced small RNAs from infected HEK293T cells to further analyze the processing of the shHTT6 guide strand. Between 100,000 and 270,000 reads per sample (n = 3) were aligned against the reference shHTT6 sequence (Figures  6B  and 6C ). The distribution and percentage of aligned reads showed that 81% corresponded to the guide strand of the shHTT6, confirming the psiCHECK-2 data. We observed cleavage of the loop in the aligned reads, in agreement with Dallas et al. ,  77  who showed that R-shRNAs have a cleavable loop that generates a 5 0 -phosphate at the 5 0 end of the guide strand and that cleavage is necessary for optimum activity.
Identification of Possible shHTT6 Off-Targets 9] [80] [81] [82]  For shHTT6, we first searched for transcripts (exon, BLAST) with partial complementarity with the two strands and identified 15 and 5 human sequences with two to four mismatches with the guide and passenger strands, respectively (Table  1 ). In parallel, we performed an RNA-seq analysis of mRNAs extracted from human striatal cultures derived from HD-iPSCs at DIV51 transduced with pCCL-H1-shHTT6 or shLUC lentivirus at DIV36. We identified 181 differentially expressed genes in shHTT6-treated cells (fold change > 1. 5; p < 0. 01; Table  S1 ). Levels for none of the theoretical off-target transcripts mentioned above were significantly altered following pCCL-H1-shHTT6 treatment. Furthermore, we used the siSPOTR software to identify a list of potential targets of the seed sequence of shHTT6 in the 3 0 UTR of genes ("miR-like effect") (Table  S2 ).
The KEGG database (http://www. genome. jp/kegg/pathway. html) provides a collection of manually drawn pathways on molecular interactions and networks for biological interpretation and mapping of transcriptomic datasets. Only five KEGG gene sets were significantly enriched (Enrichment score > 1. 5; p < 0. 05) in shHTT6-treated samples. We then entered our theoretical TOP100 off-target list as a distinct gene set to determine whether it matched our RNA-seq data. The TOP100 off-target gene set ranked only 12 th and was not significantly enriched in our samples (embryonic stem [ES] = 2. 2; p = 0. 11) (Figure  7D ). Altogether, the data demonstrate the excellent overall safety profile of pCCL-shHTT6.
qRT-PCR RT-qPCR was performed in quadruplicate with cDNAs generated from 1 mg of total RNA using the RT2 PCR Array First Strand Kit (QIAGEN, SABiosciences). qPCR was carried out in a 20 mL reaction volume containing RT 2 SYBR Green qPCR Master Mix (QIAGEN, SABiosciences) and 300 nM of both forward and reverse primers recognizing a sequence of human HTT (5 0 -CTGCACCGACCAA AGAAAGAAC-3 0 and 5 0 -CATAGCGATGCCCAGAAGTTTC-3 0 ) using a Realplex thermal cycler (Eppendorf). Values for HTT mRNA were normalized to a reference, b-actin (ACTB 5 0 -TGAAG GTGACAGCAGTCGGTTG-3 0 and 5 0 -GGCTTTTAGGATGGCAA GGGAC-3 0 ), according to following formula: RE Htt = 2 ÀCt Htt 2 ÀCt actin ; where RE = relative expression and Ct = cycle threshold. Data are expressed as mean values ± SEM.
0; Partek) using hg19 as a reference genome. To determine differentially expressed genes between groups, we quantified mapped reads using the Partek E/M algorithm, and differentially expressed genes were identified using the Partek Gene Specific Analysis (GSA) algorithm. Biological interpretations of the list of differentially expressed genes were generated using Partek Genomics Suite (v6. 6) and Gene Set Enrichment Analysis (GSEA) software from the Broad Institute (http://www. broadinstitute. org/gsea) or online (EnrichR).
Cluster generation was performed with the resulting libraries using Illumina TruSeq SR Cluster Kit v4 reagents (catalog no. GD-401-4001; Illumina) and sequenced on the Illumina HiSeq 2500 using TruSeq SBS Kit V4 reagents (catalog no. FC-401-4002). Sequencing data were processed using Illumina Pipeline Software, version 1. 84. NGS small RNA raw datasets were analyzed using CLC Genomics Workbench 8 (QIAGEN). The reads were first adaptor-trimmed using the CLC setting: minus strand, 5 0 -CCTTGGCACCCGAGAATTCCA-3 0. The first and last four bases (4N) were then clipped from the adaptortrimmed reads. All reads containing ambiguity N symbols and reads shorter than 15 nt, longer than 55 nt, or represented less than 10 times were discarded. The obtained unique small RNA reads were aligned to the reference sequence of the siHtt6-19nt construct with a maximum of 3 nt mismatches allowed.
Quantification of DARPP-32 Lesions The loss of DARPP-32 expression was analyzed by collecting digitized images of approximately 12 sections per animal (150 mm between sections) with a slide scanner and quantifying the areas of the lesions in square millimeters (mm 2 ) using image analysis software (MCID Core 7. 0, InterFocus Imaging; GE Healthcare Niagara). Lesion areas in each section were defined as regions poor in DARPP-32 staining relative to the surrounding tissue. The volume was then estimated using the following formula: volume = d * (a1 + a2 + a3. ), where d is the distance between serial sections (25 mm), and a1, a2, a3,. are DARPP-32-depleted areas for individual sections. The lesion size for each animal is expressed as the total lesion volume in 8-12 sections. The lesion volume for each group is expressed as the mean ± SEM of individual values.