dataesr/scientific-paragraphs-categorization huggingface dataset:
Already, only 10% of research projects hosted by CASD are based on a single data source. The recent global COVID-19 outbreak provides an insightful illustration in this respect. In order to deal with issues such as climate change, energy transition, and sustainable development, there is an increasing need to use data from private companies demanding a secure environment in order to be matched at a very fine spatial level with other socio-economic or health data. This entails several challenges: (1) Research projects requiring access to very large files will become more frequent, requiring requiring increased storage and computing power, which must be provided without violating legal requirements and security constraints; (2) Silos will become increasingly problematic if data are stored in multiple secure centres with different technologies and systems of governance, as is currently the case for much health data or for central banking data.
This will require either the development of cooperation networks -currently under discussion in France between CASD (which provides access to INSEE data) and the Open Data Room of the Banque de France -or more centralization into a small number of data centers, along with the fears this may arouse for citizens; (3) New matching possibilities raises the problem of long-term conservation of administrative databases in a context where the legal framework for personal data tends to limit their availability over time. In France, the applicability of the Loi Archives (Archives Act) for selecting and keeping data beyond the legal duration was discussed with the data protection authority in the context of a CNIS report. It will also be increasingly necessary to support matching requests, developing reference methodologies and tools which can be shared.
Study area First, we study the area of northern Eurasia, a region from 35 • N to the Arctic and from the Pacific Ocean to the Atlantic Ocean. The region comprises 21 % of the Earth's land area and encompasses diverse ecosystems from the steppes of central Asia to the Arctic. Forest is the major ecosystem in this region, covering 27 % of the area, followed by grasslands, which cover 16 %  (Friedl et al. , 2010). Second, to understand the forces driving the decline in burned area, we focus on the effects of drought and grazing in Kazakhstan. From 2002 to 2016, Kazakhstan had the highest rate of decline in burned area in northern Eurasia (Figs. 1, 2 ). In Kazakhstan, grassland is the dominant ecosystem and grazing is the major agricultural activity (Food and Agriculture Organization FAO Live Animals Database, 2016).
Burned area in northern Eurasia Since 2000, global burned area has been mapped by remote sensing (e. g. ,  Mouillot et al. 2014)  with different sensors and detection algorithms  (Chuvieco et al. , 2019) , leading to multiple datasets with a significant uncertainty in the magnitude of spatial distribution, interannual variability and trends in burned area  (Hantson et al. , 2016). We used the daily NASA MODIS (Moderate Resolution Imaging Spectroradiometer) dataset at a 500 m × 500 m resolution. Our MODIS-derived burned area algorithm was validated in eastern Siberia with the Landsat-derived burned area (30 m × 30 m)  (Hao et al. , 2012). The ratio of these two satellite-derived burned areas was 1. 0 with a standard deviation of 0. 5 % over 18 754 grid cells. Among other sources of variability, surface and crown fires generate significantly different spectral signals, so that the detection algorithm depends on vegetation type classification  (Chuvieco et al.
Drought The Palmer Drought Severity Index (PDSI) from the Ter-raClimate site (http://www. climatologylab. org, last access: 9 April 2021) was used to estimate drought throughout Kazakhstan  (Abatzoglou et al. , 2018). The PDSI was developed by  Palmer (1965)  and is widely used to estimate a rough soil water budget based on monthly precipitation and potential evapotranspiration, with varying soil properties for the available water content in order to account for pedological variations and species roots access to water. We used monthly PDSI data from March to July, defined as the fire season  (Roy et al. , 2008) , to compute a cumulative drought effect index. The gridded PDSI data were available at a spatial resolution of ∼ 4 km and were aggregated to the county within the study area (Fig. 1 ).
Livestock The annual population of livestock in each of the 14 provinces, each consisting of multiple counties, of Kazakhstan from 2002 to 2016 were compiled from the official agriculture statistics of the Ministry of National Economy of the Republic of Kazakhstan Committee on Statistics  (MANE, 2019). These data included yearly numbers of large horned livestock and sheep and goats at the province level, which is coarser than the county level. Livestock populations are only available at the province level, and the population was distributed proportionally to the size of the county area so that all potential drivers of fire activity could be evaluated on a common spatial scale. Thus, the livestock density for https://doi. org/10. 5194/bg-18-2559-2021 Biogeosciences, 18, 2559-2572, 2021 each county is defined as the ratio of the number of animals to the area of the county.
Land cover The MODIS land cover product (MCD12Q1) Version 6 was used to assess factors affecting the burned area in Kazakhstan. The product is available at a 500 m spatial resolution and describes the distribution of broad vegetation types. We screened these data to subset only those vegetation types considered to represent grassland vegetation (Class 10 in the MCD12Q1 dataset) from 2000 to 2016. In each year of the assessment, the number of grassland pixels was summed to enable estimates of grassland area throughout the study area.
Trends in burned areas in northern Eurasia Comparisons of our annual northern Eurasian burned areas (FEI-NE) with the latest version of the MODIS burned area product (MCD64A1, Collection 6;  Giglio et al. , 2018)  from 2002 to 2016 are shown in Fig. 3. The burned areas in these two datasets agree better in recent years after 2010. Both FEI-NE and MCD64A1 demonstrated declining trends and similar interannual variability. The FEI-NE dataset was used to analyze the driving forces for the decline in burned area in Kazakhstan (see ). Grasslands of Kazakhstan dominate changes in burned area with significant declines mostly in central and northern Kazakhstan, adjacent to the Russian border. The temporal trend in annual burned areas over all vegetation types and in grasslands in northern Eurasia and in Kazakhstan from 2002 to 2016 are shown in Fig.
Burned area The spatial and temporal extent of the area burned was examined in different ecosystems in northern Eurasia from 2002 to 2016, during which the average area burned was (2. 7 ± 1. 0) × 10 5 km 2 yr -1. The burned area in grasslands declined by 74 %, from ∼ 282 000 km 2 in 2002 to ∼ 73 000 km 2 in 2016, at a rate of 1. 3 × 10 4 km 2 yr -1. The area burned in forest showed no trend over time. Our burned area is higher than the MODIS MCD64 Collection 6, in which the average annual burned area was 9. 7×10 4 km 2 in boreal Asia during the same period  (Giglio et al. , 2018). Boreal Asia in MCD64 has a similar geographic region to our northern Eurasia. Nevertheless, the interannual variability and the trends in burned area for the two datasets are consistent (Fig. 3 ). Our results regarding burned area trends are consistent with other published results  (Giglio et al.
The major factors affecting the availability of the fuels resulting in the decline in burned area are climate, the proportion of the grassland cover, the aboveground net primary production and the livestock density. These factors interact to reduce the area burned in Kazakhstan, especially in grasslands. Data availability. All data and materials are available in the paper or Supplement. The original geospatial dataset of the burned area is large and will be made available upon reasonable request. However, a derived dataset has been used to estimate black carbon emissions from fires in the same region. This dataset has been archived on the Forest Service Data Archive website (https://doi. org/10. 2737/RDS-2016-0036; Hao et al. , 2016b). Supplement. The supplement related to this article is available online at: https://doi.
Acknowledgements. Wei Min Hao  received funding from the  US Department of State , the  US Forest Service Research and Development , and the  NASA   Terrestrial Ecology Program. Yves Balkanski  and  Philippe Ciais  received funding from the  European Union 's  Horizon 2020 Research and Innovation program  (grant no. 641816 ; CRESCENDO). Florent Mouillot received funding from the  European Space Agency FireCCI program. Financial support. This research has been supported by the  European Union 's  Horizon 2020 Research and Innovation program  (grant no. 641816 ; CRESCENDO), the  European Space Agency  ( FireCCI program ), the  Department of State , the  US Forest Service Research and Development , and the  NASA   Terrestrial Ecology Program. Review statement. This paper was edited by  Nicolas Brüggemann  and reviewed by two anonymous referees.
For each pathogen, the sequences obtained were edited and assembled using Chromas Pro1. 7. 7 software (Technelysium Pty Ltd, Australia). HBV (or HCV) genotypes were determined by phylogenetic analysis using a set of reference HBV (or HCV) sequences available from the NCBI GenBank or from our institute's local HBV (or HCV) sequence bank of the population of Marseille and the Max-Planck-Institut für Informatik 12 sequence databases, using the Mega 7. 0 software ( https://www. megasoftware. net ). In these websites, the nucleotide sequences obtained were simultaneously translated into amino acid (aa) sequences then compared with reference HBV (or HCV) sequences of the same genotype in order to determinate aa substitutions previously described in association with reduced antigenicity or susceptibility to anti-HBV (or HCV) drugs.
2. The stepwise regression procedure and likelihood-ratio tests were applied to determine the final model. 1  Number of individuals for whom data was available. 2 Median of the variable is used for analysis. 3 Low prevalence was not entered into multivariate model. 4 Based on the WHO classification, "underweight" is defined as having a body mass index (BMI) below 18. 5, "normal" corresponds to a BMI between 18. 5 and 25, "overweight" corresponds to a BMI ≥25, and "obese" refers to those with a BMI ≥30. Source: WHO. Body mass index -BMI http://www. euro. who. int/en/health-topics/ disease-prevention/nutrition/ahealthy-lifestyle/body-mass-index-bmi ; (2019). Bold lines indicate the variables recruited in initial multivariate model.
Genotypic patterns of HBV ( Fig. 1 & supplementary Table 4) HBV genotyping was performed on 44 available sera from 44 HBsAg-positive subjects. We succeeded in amplifying the gene from 16 subjects (36%). The phylogenetic tree showed that HBV sequences of our homeless people clustered with genotype A (HBV-A), D (HBV-D) or E (HBV-E) reference sequences. Thus, we detected the HBV-A in 2 cases (12. 5%; A2 = 2), HBV-D in 9 cases (56. 2%; D1 = 3, D2 = 1, D3 = 1, D4 = 2, D7 = 2), and HBV-E in 5 cases (31. 2%). Overall, the mean ( ±SD, range) nucleotide identity between our 16 HBV sequences characterised and their best match available in the GenBank and in our local sequence database was 98. 7% ( ±0. 8%, range 96. 8% -99. 7%) and 97. 1 ( ±1. 0%, range 95. 8% -98. 7%), respectively. The HBV sequences recovered from homeless people were scattered across the phylogenetic tree, and were neither clustered between each other (with one exception) nor more frequently clustered with locally-acquired sequences compared to GenBank sequences.
Genotypic patterns of HCV ( Fig. 2 & supplementary Table 5) HCV RNA could be amplified from six of the 30 (20%) sera available from 30 anti-HCV-positive subjects. Two of six (33. 3%) HCV sequences belonged to subtype 1a (HCV-1a), two (33. 3%) to subtype 1b (HCV-1b), one (16. 6%) to subtype 3a (HCV-3a), and one (16. 6%) to subtype 4d (HCV-4d). Overall, the mean ( ±SD, range) nucleotide identity between the six HCV sequences characterised in this study and their best match available in the GenBank and in our local sequence database was 96. 2% ( ±1. 5%, range: 93. 6% -97. 8%) or 94. 2% ( ±1. 9%, range: 92. 3% -96. 7%), respectively. The HCV sequences recovered from homeless people were scattered across the phylogenetic tree and were neither clustered close to one another nor more frequently clustered with locally acquired sequences than with GenBank sequences.
After developing the technique on a dataset of long-term simultaneous scalp and intracranial recordings we confirm its practical clinical applicability on an independent scalp EEG dataset. Our results suggest that scalp zero-crossing OPEN 1 Bioelectrics Lab, Institute of Brain and Spine (ICM), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 47 Boulevard de l'Hôpital, 75013 Paris, France. 2  Bioserenity, Institute of Brain and Spine (ICM), Paris, France. 3  Sorbonne University, UPMC Univ, Paris 06, 75005 Paris, France. 4  Department of Neurology, Ain-Shams University, Cairo, Egypt. 5 Department of Emergency Medicine, Medical University of Gdańsk, Gdańsk, Poland. 6 Department of Neurology and Epileptology, Medical Centre for Postgraduate Education, Warsaw, Poland. 7 Laboratoire D'Imagerie Biomédicale, (INSERM U1146UMR7371 CNRS, Sorbonne université), Campus des Cordeliers, 15 rue de l'Ecole de Médecine, 75006 Paris, France.
Methods Subjects and recording procedures. Scalp EEG and iEEG recordings of two groups of subjects were studied retrospectively: Group A comprised 16 TLE subjects hospitalized in the Freiburg Epilepsy Center between 2003 and 2009 (8 male and 8 female, aged 11-63 years, mean 28 years) selected from the Epilepsiae database  28  according to the following criteria: (1) electrophysiologically confirmed presence of an epileptic focus in the temporal lobe, (2) availability of a 72 h continuous segment of dual iEEG and scalp EEG recordings with (3) full 10-20 scalp electrode coverage + T1/T2 (FT9/FT10) electrodes. Table  1  summarizes their clinical and demographic characteristics. The signals were divided into non-overlapping epochs of 60 s length. Analysis was performed over an average of 64. 8 h of recording per subject (3889 epochs, range 33.
We have studied these recordings previously with different methods  27  and the current Group B was obtained by the rejection of subjects with frontal lobe epilepsy from the dataset (see Pyrzowski et al. 27  for further clinical and demographic information). The final 5 min (eyes closed, posthyperventilation) of standard scalp EEG recordings with full 10-20 scalp electrode coverage were used for the analysis. No recording contained overt ictal electrographic activity. The review summaries of EEG recordings were converted to a 4-level scale (the "EEG-score"): (0) no abnormalities, (1) normal EEG variants, (2) non epilepsy-specific abnormalities (including background slowing), and (3) epilepsy-specific abnormalities (IEDs)  27. All data were collected during a routine clinical workup. All patients (or in the case of minors, their legal representatives) provided written statements of informed consent for research use of these data.
Interestingly, assessment with cross-patient templates proved highly effective and was only in rare cases outperformed by within-patient templates (black arrows) whose performance was much more variable. This finding points again to the possibility of generalization of IED-related zero-crossing patterns between patients but also reflects the larger repertoire of cross-patient templates (for 13% channels a within-patient template was not available). External validation: epilepsy vs control classification from standard scalp EEG recordings. We next used an independent dataset of standard scalp EEG recordings to validate and test the robustness of the zero-crossing method. As in Pyrzowski et al. 27  we addressed the power of discrimination between subjects with TLE (n = 19) and controls without epilepsy (n = 27).
Discussion Basing on the study of relations between concurrent iEEG and scalp EEG signals we propose the use of the zero-crossing pattern representation of scalp EEG to detect scalp signatures of intracranial IEDs. The proposed computational method has the potential to increase the clinical value of scalp EEG in epilepsy diagnosis and was externally validated on an independent dataset of standard scalp EEG recordings. Our main findings are that: (1) Zero-crossing patterns are a reliable single-trial scalp EEG biomarker of intracranial IEDs including some apparently non-propagating MT IEDs (median likelihood score-related SNR > 0 dB). (  2 ) Most scalp EEG waveforms associated with reproducible zero-crossing patterns are amplitude-wise below background (median scalp voltage related SNR < 0 dB).
Other limitations of our approach include the inability to detect intracranial IED propagation in forms other than spikes (e. g. slow waves) as well as the general limitations imposed by unsupervised/semi-supervised automatic processing of large volumes of data. In particular, the proposed method needs to be further validated through systematic comparison to standard visual EEG assessment in both scalp and iEEG signals. To confirm its localizing value and specificity, the method needs also to be assessed in epileptic syndromes other than TLE (frontal lobe epilepsy, which was underrepresented in our dataset, would be of prime interest) as well as in patients with non-epileptogenic intracranial lesions. Further study is also needed to assess whether the complex information contained in individual templates permits the reconstruction of deep sources and propagation pathways.
Data availability The dual EEG dataset analyzed during the current study (group A) is available in the Epilepsiae repository, http:// www. epile psiae. eu/. The validation dataset (Group B) is not publicly available but can become available from the corresponding author on reasonable request with the permission of JJ. | (2021) 11:4128 | https://doi. org/10. 1038/s41598-021-83337-3.
S7  and S8 ). Several interesting colocalizations of different types of cracking QTLs were observed (Figs. S2-S4 ). These co-localizations have to be considered cautiously since most of these QTLs were inconsistently detected, explained low levels of phenotypic variance, and were detected with large confidence intervals, even with the improvement allowed by the use of models 1 and 2. The complexity of the genetic determinism of cracking tolerance/susceptibility was further confirmed when conducting two-linked QTL analyses, since two putative QTLs were detected in five different LGs, including the three major QTLs previously mentioned (Table  7  and Fig. 5 ). In former studies conducted on populations R × L and R × G on other highly complex traits, such as FW and FF  39  , the presence of two QTLs per LG was also reported on almost all LGs when conducting multi-year analyses with MultiQTL software.
Statistical analyses In order to illustrate the between-year variability in terms of rainfall accumulated during the harvest period, the mean amount of rainfall that all progenies (or hybrids) within each population received during the week or the two weeks before harvest, for the period 2008-2014, was calculated. Basic descriptive statistics were calculated for each trait, year, and population considered. Raw data were visualized through the use of box-plots. Cracking tolerance was computed with percentage of count data, a priori following a multinomial distribution. Hence, an arcsin (rootsquare) transformation was applied in order to stabilize the variance and to estimate genetic variances and heritabilities. We calculated Spearman coefficients of correlation (by using untransformed or raw data) in order to assess inter-year variation for each type of cracking tolerance and the relationships between the three types of cracking as well as cracking tolerance and other fruit quality-related traits, such as FW and FF.
In order to assess the effect of rainfall, FW, and FF on cracking tolerance, simple linear models were tested by using different covariates related to rainfall, as well as the values of FW and FF measured for each genotype. The linear models were tested with data produced from 2008 until 2014. From 2015 onwards, serious damage was observed on fruits, provoked by the invasive pest Drosophila suzukii  61. The comparison of QTL results for cracking tolerance, produced with data collected before and after 2014, did not show significant differences. However, the evaluation of traits such as FW and more significantly, FF, became much more complicated, and hence these traits were not considered after 2014. Meteorological data were extracted from a local weather station and only the rainfall occurring during a period of 14 days before harvest was deemed to have a putative influence on fruit cracking.
These two types of models were adjusted: Model 'rainfall': Z ij ¼ μ þ α i þ G j þ X k¼K k¼1 c k R kij À Á þ ε ij Model 'rainfall & fruit quality': Z ij ¼ μ þ α i þ G j þ X k¼K k¼1 c k R kij À Á þ X l¼L l¼1 ðd l Q lij Þ þ ε ij where Z ij was PE, SE, or FS cracking incidence, for year i and genotype j, µ was intercept, α i a year i fixed effect, G j a genotype j random effect, R k a quantitative covariate k related to rainfall (correspondent parameter c k ), Q l a quantitative covariate l related to fruit quality (correspondent parameter d l ), and ε ij was a residual error. Analyses were performed with the statistical SAS 9. 3 software (SAS Institute, Cary, NC). In a first step (model 'rainfall'), different combinations of rainfall-related covariates were included allowing for the integration of correlated but not collinear variables.
Finally, a measure of between-year stability, which can be viewed as a sort of broad-sense heritability (H BS ), was estimated for each trait and population, as described in  32, 33. When considering only genotype and year effects, the VARCOMP procedure of SAS was used. Mean squares were estimated in order to calculate genetic variances. A second type of heritability was calculated by using the selected models with covariates. As the latter are quantitative and not categorical variables, VARCOMP procedure cannot be used and instead, we performed PROC MIXED. In this case, an estimation of the residual variance was generated and used for the heritability calculations. For each model, by using the OUTP command within the procedure MIXED, we generated predicted values for each genotype, conditional to the genotypes and years, considered as random effects.
QTL detection and mapping were carried out using MultiQTL v2. 6 software and the multiple interval mapping (MIM) approach (Haifa, Israel, 2005; http://www. multiQTL. com). Parameters for each QTL, namely LOD (logarithm of the odds ratio) values, position in cM, PVE, and the substitution effect (d 2 ) were estimated according to  33. Two types of analyses were performed. First, each year was analyzed independently for each trait. Second, the multi-environment option was used to combine all years of study for each trait and population, in order to increase the accuracy of the QTL detection. When conducting multi-year analyses, values of PVE and d 2 were estimated for each year. However, for ease of reading, only the mean PVE and d 2 values across years were presented to synthesize multi-year analyses.