dataesr/scientific-paragraphs-categorization huggingface dataset:
Acquisition parameters Ex vivo MRI data were acquired on a 11. 7 Tesla BioSpec preclinical scanner (Bruker, Germany), with experimenter blinded to the genotype of animals. A Rapid Acquisition with Refocused Echoes (RARE) sequence was used to collect the series of T1-weighted volumes with the following parameters: RARE factor = 16, effective TE/TR = 8. 37/2 500ms, field of view (FOV) = 19. 2×15. 2×15. 2 mm 3 , acquisition matrix = 240×190×190, spatial resolution = 80×80×80 µm 3 , 1 average. This sequence (1h30min) was repeated with 32 different inversion times ranging from 150 to 2300 ms for a total scan duration of 44h16min. A multi-spin multi-echo pulse sequence was used to collect the series of T2weighted volumes with the following parameters: TR = 510ms; 30 TE ranging from 5 to 155ms in steps All data were pre-and post-processed using the Ginkgo toolbox freely available at https://framagit.
A hybrid optimization scheme composed of a sequence of a Non-Linear Programming optimizer with constraints followed by a Monte-Carlo-Markov-Chain optimizer was implemented to robustly fit the MWF map. All diffusion and quantitative maps were finally co-registered to the Turone Mouse Brain Template and Atlas using the previously calculated diffeomorphic transformations to perform statistics over control and mutant mice. Pre and post-processing of MRI data were performed with the experimenter blinded throughout the process.
Statistical analysis. To assess regional differences at the voxel level of dMRI, NODDI and MWF metrics, between controls and TgNotch3 R169C mice, smoothed maps were compared using a two-sample t-test implemented in Statistical Parametric Mapping 8 for voxel-wise comparison. The significance level applied was set at p = 0. 05 (t(20) = 1. 7274 false discovery rate (FDR)-corrected), with a minimum cluster size set at 25 voxels. From maps generated by the analyses and the mouse brain template, we made brain plots with the xjView plugin (https://www. alivelearn. net/xjview/). To assess the amplitude of differences within each of the major WM tracts, we performed a region of interest analysis. Clusters revealed by the voxel-wise comparisons were identified using the mouse brain atlas and an in-house pipeline developed with MATLAB Simulink 10b (The Mathworks, USA).
Clusters identified were binarized and regions of interest obtained were used to extract each metrics with the REX plugin (https://web. conn-toolbox. org/). Statistical significance was determined using a 2-way ANOVA followed by a two-stage linear step-up method of Benjamini, Krieger and Yekutieli to correct for multiple comparisons by controlling the False Discovery Rate (<5%) (GraphPad Prism 9 software). Significance for electron microscopy data was calculated using an unpaired Student's t test (GraphPad Prism 9 software).
Design of the MRI study. We used the TgNotch3 R169C line, a well-characterized mouse model of CADASIL, in which mutant mice aged 20 to 22 months exhibit asymptomatic WM abnormalities in the absence of lacunar infarcts  10. Therefore, we assumed that at this age mutant mice recapitulate the early stage of WM changes in patients. We collected in the same animals one dMRI dataset with a multiple-shell Q-space sampling strategy and two T1-weighted and T2-weighted qMRI relaxometric datasets at 11. 7T. The dMRI and the relaxometry data sets were acquired with 160-µm and 80-µm isotropic resolution respectively over 3 consecutive days (Fig  1A ). Figure  2  shows representative whole brain tractograms highlighting our ability to accurately delineate WM fiber tracts at ultra-high resolution in the mouse brain.
From dMRI, we calculated the Generalized Fractional Anisotropy (GFA), a sensitive proxy of the loss of WM tract integrity. To probe the nature of microstructural abnormalities, dMRI data sets were then processed with the NODDI model to extract the neurite (axons and dendrites) density index (NDI), the isotropic volume fraction (ISOVF) corresponding to the extracellular free water and the orientation dispersion index (ODI). The relaxometry datasets were computed using a 3-compartment biophysical model to quantify the myelin water fraction (MWF), a direct metric of the myelin content. From these analyses, we obtained quantitative maps that were co-registered to a common template (Fig 1B ).
The use of ultra-high-field (11. 7T) diffusion and qMRI in the frame of a prolonged acquisition protocol lasting 3 consecutive days allowed us to collect for each individual an extended set of dMRI volumes at the mesoscale level, at very high diffusion sensitizations (8000s/mm²) and with many diffusion directions (175 in total) along with an extended set of qMRI relaxometric volumes with an unusual large number of inversion and echo times. The scanning of each animal in a single session has eliminated the need to remove the sample from the scanner, thus avoiding the risk of motion artifacts between the acquisition of the different datasets. Combining dMRI and qMRI is a key point that has allowed us to accurately characterize the cytoarchitecture and myeloarchitecture of WM tracts at the voxel level.
Sources of Funding Electron microscopy images were acquired at the  Imagoseine imaging core facility , member of the  France BioImaging  infrastructure supported by grant  ANR-10-INBS-04  from the  French National Research Agency. This work was supported by  Fondation Leducq (Transatlantic Network of Excellence   for the Study of Perivascular Spaces in Small Vessel Disease ,  16 CVD 05 ) to AJ and the  National Research Agency, France  ( ANR-16-RHUS-0004 ) to CP & AJ.
Post-processing Data were post-processed with Matlab (2020) (MathWorks, Natick, Massachusetts, United States). Accelerations were filtered with a lowpass filter (Butterworth, cut-off frequency 600 Hz, 4th order)  [6]. The analysis focused on the first impact between the impactor and the padding, excluding potential rebounds. The impact was defined as the time at which the resultant acceleration exceeded 4 g. Maximal accelerations (Amax) and 3 ms accelerations (A3ms, being the maximal acceleration exceeded during at least 3 ms) were computed from resultant accelerations. For each test, the ratio between Amax and A3ms  (rAmaxA3ms) was computed to characterize the acceleration curve shape. Impact speeds were calculated by integrating vertical accelerations. All statistical analyses were conducted on Amax values.
Data regarding the age, county and region were collected (Table  1 , Fig. 1 ). The samples were refrigerated and transported on the same day to the laboratory. The blood samples were centrifuged at 3000 rpm for 10 min, and the sera were stored at -20 °C until further analysis. Toxoplasma gondii-specific IgG antibodies were detected by enzyme-linked immunosorbent assay (ELISA) using a commercial kit (Chekit Toxotest Antibody ELISA, Idexx-Bommeli, Switzerland) following the manufacturers' instructions. The test results were interpreted as positive, doubtful or negative.
Bioassay study During slaughtering, paired samples of diaphragmatic muscle (5-25 g) and blood were collected randomly from 328 lambs from seven counties from four regions of Romania: central (Mureș, Harghita, Covasna), northwestern (Cluj, Maramureș), western (Arad) and southeast (Buzău) (Fig. 1 ). The lambs were 2-4 months old and weighed 8-25 kg. They were slaughtered during the Easter holiday and intended for familial consumption. A questionnaire was prepared for collection of data regarding the age and region of origin of the animals (Table  3 ). The samples were transported under refrigerated conditions to the laboratory. Serum samples were tested by modified agglutination test (MAT) within 24-48 h. Diaphragmatic tissue samples were kept under refrigerated conditions (4 °C) until bioassay in mice was performed.
Polymerase chain reaction (PCR) DNA was extracted from mouse brains and aborted fetus tissues (heart and brain) using a commercial kit (Isolate Genomic II DNA Kit, Meridian Bioscience, USA) according to the manufacturer's protocol. Positive and negative controls were represented by DNA obtained from T. gondii RH strain and tissue from free-pathogen Swiss mice, respectively. Toxoplasma gondii DNA was amplified by conventional PCR using the pair primers Tox 4 (5′ CGC TGC AGG GAG GAA GAC GAA AGT TG 3′) and Tox 5 (5′ CGC TGC AGA CAC AGT GCA TCT GGA TT 3′) that amplify the 529-bp RE (REP529, GenBank accession no. AF146527) as previously described  [20]. Positive and negative controls and distilled ultrapure water were included in each run.
After an initial 15 min denaturation at 95 °C, 35 cycles were performed as follows: 30 s at 94 °C, 3 min at 61 °C, 30 s at 72 °C. This was followed by a final extension for 30 min at 60 °C. PCR products were diluted 1:10 with deionized formamide. One microliter of each diluted PCR product was mixed with 0. 5 µl of a dye-labeled size standard (ROX 500, Applied Biosystems, Life Technologies, Carlsbad, CA) and 23. 5 µl deionized formamide (Applied Biosystems). This mixture was denatured at 95 °C for 5 min and then electrophoresed using an automatic sequencer (ABI PRISM 3130xl, Applied Biosystems). The sizes of the alleles in bp were estimated using GeneMapper analysis software (version 5. 0, Applied Biosystems). The microsatellite data were used for producing an unrooted neighbor-joining tree using Populations 1.
Statistical analysis Point estimates and 95% confidence intervals (95% CI) for the anti-T. gondii antibodies and T. gondii DNA were calculated. These parameters were determined overall and by age group (lambs < 1 year; adults ≥ 1 year) and for each region (central, northwest, west, southeast). Also, the estimated age of lambs was recorded (between 2 and 8 months), and six groups were formed (2 months; 3 months; 4 months; 5 months; 6 months; 8 months). The difference in prevalence among groups was statistically analyzed using a Chi-square test of independence. A P-value of < 0. 05 was considered statistically significant. Data were processed using EpiInfo 2000 software (CDC, Atlanta, GA, USA) (http:// wwwn. cdc. gov/ epiin fo).
EC data We used half hourly eddy-covariance and meteorological data at 14 forest sites. Data were downloaded from several ICOS datasets: WarmWinter2020 80 , level 2 ICOS data for 2021 (and before when available) and NearRealTime (NRT) for 2022 (both from https://data. icos-cp. eu/, last access 17-04-2023). NRT data were cleaned and checked following Sabbatini et al. (2018)  81  and Pastorello et al. (2020)  82. Subsequently, average fluxes were corrected for periods of low friction velocity (u*-threshold method), following Papale et al. (2006)   83. All gaps were then filled using the marginal distribution sampling (MDS) algorithm from Reichstein et al. (2005)   84. Finally, NEE partitioning into GPP and ecosystem respiration was done using the night-time based temperature response of NEE following Reichstein et al.
84. u* calculation and filtering, gap-filling and fluxes partitioning were all performed using the REddyProc R package  85. The sites are listed in Table  S8  and an extensive analysis of the EC data is shown in Supplementary section F. Anomalies are calculated based on 2016-2021, excluding 2018. We note NEE from the atmospheric perspective, i. e. uptake by plants is negative and positive NEE indicates emissions into the atmosphere.
NIRv data We used NIRv calculated from the Bidirectional reflectance distribution function-corrected surface reflectances  40, 87  at 0. 5 by 0. 5 degree from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. To account for forest growth, we used linear detrended data, where the slope was calculated over the NIRv data from 2001-2021, which is the same period the anomalies were calculated over. Supplementary Material G contains a more in-depth description of the NIRv data used and the calculation of the GPP anomaly from NIRv data.
Acknowledgements We acknowledge  NWO/ENW  (file number 2023. 003) and  SURF  for supercomputing facilities. We furthermore acknowledge the PARIS project funded by the  European Union  under the Grant Agreement nr  101081430 ,  NWO  ( Vidi grant   VI. Vidi. 213. 143 ) and the  Ruisdael Observatory , which is (partly) financed by the  Dutch Research Council (NWO , grant number  184. 034. 015 ) for financial support. Furthermore, we thank the  ICOS Carbon Portal  for hosting the CTE-HR fluxes. We kindly acknowledge the provision of data from eddy-covariance sites 80 , as well as from atmospheric sites 86. The measurements at WAO are funded by the  UK's National Centre for Atmospheric Sciences (NCAS). CO 2 observations at Jungfraujoch are supported by  ICOS Switzerland  ( ICOS-CH) Phase 3  ( Swiss National Science Foundation , grant  20F120_198227 ).
Data availability MODIS reflectance data can be downloaded from https://modis. gsfc. nasa. gov/data/dataprod/mod13. php (last access 19/06/23) and GFAS data from https://ads. atmosphere. copernicus. eu/cdsapp#!/dataset/ cams-global-fire-emissions-gfas?tab=overview (last access 19/06/23). Note that GFAS data for Europe after 2016, as well as CTE-HR NEE from SiB4 is available from https://doi. org/10. 18160/20Z1-AYJ2. Atmospheric CO 2 mole fractions used in this study are available at https:// doi. org/10. 18160/CEC4-CAGK and https://meta. icos-cp. eu/objects/ 2ESjwQy1qQRMEtcpiYPun2RO, (last access: 21/06/23) and EC measurements at https://doi. org/10. 5281/zenodo. 8056635.
Strategic Environmental Assessment (SEA) of land-use planning is a fundamental tool to minimize environmental impacts of artificialization. In this context, Systematic Conservation Planning (SCP) tools based on Species Distribution Models (SDM) are frequently used for the elaboration of spatially exhaustive biodiversity diagnostics. Despite the paradigm of "garbage in -garbage out" that emphasises the importance of testing the suitability of data for SDM and priority conservation areas, the assessment of database sources remains relatively rare. In addition, the lack of practical recommendations for the use of open-access databases by SEA stakeholders remains a problem. The aim of this study is to explore the quality of data sources that can be used by stakeholders to assess priority conservation areas in SEA.
The study was done using data for nine taxonomic groups (commonly used in inventories for environmental impact assessment) and three databases available to SEA stakeholders. Three local administrative entities in very different socio-ecological contexts were used to examine three main issues : (i) the suitability of local versus national databases for assessing conservation priorities, (ii) differences among taxonomic groups or territories in terms of the suitability of databases, (iii) the importance of the quality of databases for the application of SDM to assess priority conservation areas. Our study provides several clear messages for potential users of open-access databases. First, the need for prudence in the interpretation of biodiversity maps. Second, for SDM, the collection of individual databases at the national scale is necessary to complete local data and ensure the suitability of models in a local context.
, 2020; Tulloch et al. , 2019; Baker et al. , 2021; Boileau et al. , 2022). Among these methods of biodiversity modelling, Species Distribution Models (SDM) are widely used to predict suitable habitat for species based empirical observations  (Guisan et al. , 2017; Zurell et al. , 2020)  and are increasingly used in conservation planning  (Guisan et al. , 2017; Domisch et al. , 2019; Baker et al. , 2021). Systematic Conservation Planning (SCP) tools are also particular pertinent to identify priority biodiversity stakes and avoid the adoption of an ad hoc approach  (Margules and Pressey, 2000; Pressey and Bottrill, 2008)  in order to inform SEA  (Tulloch et al. , 2019). The management of databases and their use for conservation planning is a critical issue for the application of such methods to practical conservation planning.
The databases available for stakeholders are often limited because of data sensitivity or ownership issues, although more and more programmes contain data that are publicly available and use of them can be made without any particular attention to their quality  (Costello and Wieczorek, 2014; Tittensor et al. , 2014). Surprisingly however, despite the prevailing recognition of the "garbage in -garbage out" that emphasises the critical importance of the quality of data  (Sanders and Saxe, 2017; Canbek, 2022) , an examination of data suitability is relatively rare in local conservation planning  (Rondinini et al. , 2006; Hermoso et al. , 2015a). In this context, some authors argue the necessity of examining the sensitivity of model results to the nature of the datasets that are used  (Sanders and Saxe, 2017; Clare et al.
, 2019; Velazco et al. , 2020). Another particularly important point that can influence distribution modelling is the spatial extent of the data, and in particular the question of whether to use only local data or those collected on a larger scale  (Baker et al. , 2021; Meyer, 2007). The choice and possible combination of data sources is part of this problem due to the fact that they often vary considerably in their design, the gradients covered, and potential sampling biases  (Fletcher et al. , 2019; Boyd et al. , 2023). Basically, the use of available databases requires a rigorous test of their quality and pertinence  (Zuckerberg et al. , 2011) , especially when used for analyses such as SDM  (Tulloch et al. , 2016; Domisch et al. , 2019). As recognised by  Clare et al. , (2019) , the lack of practical recommendations for the use of databases that differ in terms of their quality and pertinence by public authorities or other institutions remains a serious problem.
The overall goal of this study is to test the influence of different database sources that can be used by stakeholders to map priority conservation areas in SEAs based on SCP. To do so, we studied three local administrative territories that occur in different socio-ecological contexts in France. The study has three main objectives. First, in terms of the quantity of data available for nine taxonomic groups commonly used in naturalist inventories in environmental impact assessment studies, we assess the variation in the suitability of the available databases at three scales (local, regional and national) for application of SDM. Second, by focusing on two taxonomic groups with different dispersion capacities, home range sizes and environmental response scales, we explore the influence of databases for the application of SDM to assess priority conservation areas.
-Content of available databases We focused on nine taxonomic groups commonly used for naturalist inventories for environmental impact assessment studies: Amphibia, nesting Aves (hereafter name Aves), Chiroptera, Flora, Mammalia aptera (hereafter name Mammalia), Orthoptera, Odonata, Papilionidae and Reptilia  (Bigard et al. , 2017; Guillet et al. , 2019; Iorio et al. , 2022). We selected three open-access databases containing these groups that can be widely used by SEA stakeholders for the assessment and hierarchy of conservation priorities (Figure  2 , step 1. 1). The first of these databases concerns the French Natural and Landscape Information System (SINP) that is structured at the regional scale in France. This database is composed of opportunist observations and only contains presence data for taxa for which identification is confirmed by experts  (Jomier et al.
, 2018). Access to this database requires a data extraction request to the regional administrative structure in charge of the database. Generally, a special request is necessary above a 10km buffer zone around the study site (maximum 2000 km2), the data remains accessible only locally. Each site has its own database (see Appendix A. 1). The second database is the Global Biodiversity Information Facility (GBIF) an international platform for the provision of biodiversity data that is based on information collected from various databases  (Telenius, 2011). It is composed of observation data that are not based on protocols and for which presence data and identification are not subject to expert confirmation. The data downloading is autonomous from the website (see Appendix A. 1). The third database is a French biodiversity monitoring scheme (Vigie Nature) dedicated to assess spatiotemporal populations trends.
Within this monitoring scheme data collection is based on a standardized biodiversity survey. Despite local spatial distribution heterogeneities, the sampling plan ensures a representation of the current national distribution of habitats and landscapes across France  (Julliard and Jiguet, 2002; Mariton et al. , 2022). Homogeneity in identification criteria and compliance with the protocol are ensured by offering training to volunteers. This database is composed of presence/absence and abundance data. Access to these databases requires a data extraction request to the person in charge (see Appendix A. 1). These three databases were combined in two ways: All databases (i. e. SINP, GBIF and Vigie Nature) used in section 2. 3 (i. e. for assess which taxa are enough documented within each dataset) and National databases (i.
e. GBIF and Vigie Nature) used specifically in section 2. 4 (i. e. for test the effect of database sources on SDM performance). The databases were collected for continental France except for the SINP that was collected in 10km buffer zone around the study sites due to the access restrictions explained above. The databases were collected over a period of 10 years (i. e. from 01/01/2010 to 31/12/2020) and data with spatial inaccuracy greater than 50 meters were not considered. We made a series of operations to standardize, correct and homogenize species names, transform data into occurrences, limit their spatial biases and identify the presence of species into each study site (Appendix A. 1).
-Quantitative description of databases for SDM For the nine taxonomic groups, four metrics were selected to quantitatively describe the amount and thus the suitability of each database (i. e. SINP, GBIF, Vigie Nature and All databases) for the realization of SDM in presence-only (Figure  2 , step 1. 2): (i) the number of species observed in the study site; (ii) the proportion of species with < 15 observations which represents the minimal threshold for the utility of SDM with more accurate predictions than in a random model  (Støa et al. , 2019) ; (iii) the proportion of species with between 15 and 50 observations, i. e. the minimum number of presences recommended for SDM  (Merow et al. , 2014 in Guisan et al. , 2017) ; (iv) the proportion of species with > 50 observations, i. e. highly suitable for modelling  (Støa et al.
, 2019). These metrics were calculated at three different scales: (a) for each territory including a 10km buffer zone (local scale) that is the maximum extent for a SINP data request; (b) on a regional scale that is used for the structure of biodiversity data in France; (c) for continental France. SINP database is analysed individually only at the local scale due to the previously mentioned restriction of access, nevertheless it integrated the three scales of the All databases.
-Systematic conservation planning process For the three study sites, we identified priority conservation areas with a Systematic Conservation Planning (SCP) tool based on Species Distribution Models (SDM) (Figure  2 , steps 2. 1 and 2. 2). Several variants of SDM were made using different database sources (i. e. GBIF, Vigie Nature and National databases) and two methods of generating pseudo-absences (named individual database or mixed databases). Among the nine taxonomic groups studied above, only Aves and Papilionidae taxa were analysed to compare the tests in the SCP process. The data available in France for these groups seems to be sufficient to realize SDM with each database. The use of these two groups allows for a comparison between one group of highly mobile taxa with a large home range (Aves) and another group with a smaller home range and whose movement closely tracks local environmental variation (Papilionidae).