dataesr/scientific-paragraphs-categorization huggingface dataset:
Internationally, there are a number of standard methods used to measure soil properties and several methods may have been applied to measure the reported soil properties. In this regard, no conversion has been made and only raw data are reported in the database. However, we have supplied the references for all data (where available) that can be used to ascertain which methodologies were used, if so desired. Although supplying such information for each soil property may facilitate the use of the database, it would have required considerable additional work that could not be performed at this stage of development. Such additions could form the basis of a second version of the database that any readers should feel free to commence. The uncertainty with respect to the effect of measurement techniques on quantifying the infiltration process itself may be analyzed from the SWIG database as it provides information on the type of measurement technique used.
This analysis is again beyond the scope of this paper. Potential error and uncertainty sources with respect to the use of different measurements are discussed in the Supplement. The uncertainty of estimated soil hydraulic properties from infiltration measurements may be strongly controlled by the person performing the experiment but may also be due the different measurement windows of the methods in terms of measurement volume. The SWIG database provides information to quantify uncertainties introduced by difference in measurement volume and this analysis will be closely related to the assessment of the representative elementary volume, REV (see, for example, the work of Pachepsky on the scaling of saturated hydraulic conductivity). Careful interpretation of the data, with respect to the details of the experimental and soil conditions, is also required when utilizing the SWIG database.
For instance, the cases of soils coded 1211-1420 may at first seem odd, as they display very low infiltration rates for soils of a very high (> 95 %) sand content; however, these unusual findings are explained by the soils being recorded as displaying water repellant characteristics. Another example is estimated values of K sat from clayey soils showing high values of K sat (e. g. , soils coded 3746 to 3833 in the SWIG database). The K sat values for these soils were obtained using the single-ring infiltrometer method  (Gonzalez-Sosa et al. , 2010; Braud, 2015; Braud and Vandervaere, 2015)  and were conducted in the field under ponded conditions, with vegetation cut but roots left in place. Macropores could have been activated, leading to an infiltration rate much higher than expected for clayey soils.
Research potentials of the SWIG database We envision that the SWIG database offers a unique opportunity and information source to (1) evaluate infiltration methods and to assess their value in deriving soil hydraulic properties, (2) test different models and concepts for point-scale and grid-scale infiltration processes, (3) develop pedotransfer functions to estimate soil hydraulic properties such as the Mualem-van Genuchten parameters, (4) identify controls on infiltration processes, (5) validate global predictions of in-filtration from land surface models, (6) study more complex processes like preferential flow in soils, and (7) highlight the state-of-the-art understanding of the relationships between infiltration and several soil surface characteristics; for example, the SWIG database has already contributed to the scope of  Morbidelli et al.
(2018)  to advance the knowledge of infiltration over sloping surfaces. We are confident that the SWIG database is just a first step in collecting and archiving infiltration data and we expect that increasing amounts of data will become available in the near future. These data will be archived in the SWIG database and thus made available to the worldwide research community. In this regard, we are interested in receiving existing or newly measured infiltration curves and for this purpose the corresponding author will serve as point of contact or data can be made available through the International Soil Modeling Consortium, ISMC (https: //soil-modeling. org/, last access: 1 July 2018), for further archiving in the SWIG database.
Conclusion We have collected 5023 infiltration curves from field experiments from all over the world covering a broad range of soils, land uses, and climate regions. We estimated saturated hydraulic conductivity, K sat , and sorptivity from more than 3000 infiltration curves and compared estimated K sat values with values from different databases published in the literature. We showed that contrary to the assumption made in many land surface and global climate models, texture is not the main controlling factor for K sat. In addition, the variability of K sat derived from these field measurements is considerably larger than reported in the literature. The collected infiltration curves were archived as the SWIG database on the PANGAEA platform and are therefore available worldwide. The data are structured into *.
10 0. 001 0. 38 0. 06 Mean weight diameter (mm) 258 5 1 0. 10 2. 75 1. 0 Gravel (%) 243 5 18 0 92 15 Sodium adsorption ratio 156 3 5 0 89 1 Soil water content at FC (cm 3 cm -3 ) 74 1 0. 28 0. 12 0. 54 0. 27 Soil water content at PWP (cm 3 cm -3 ) 64 1 0. 18 0. 05 0. 36 0. 20 Geometric mean diameter (mm) 73 1 0. 6 0. 4 0. 8 0. 6 properties, number of data entries in the database (out of 5023 soil water infiltration curves in total), and their statistical description.
e. , cropped land, irrigated land, dryland, and fallow land, is the most frequently reported land use in the database with about 53 % (2019 out of 3818) of all land uses. With 22 %, grasslands are the second most frequently represented land use type. Pasture with 6 % and forest with 5 % are ranked as the third-and fourth-largest reported land use types. The 18 re-maining land use types all together cover only 545 experimental sites (less than 15 %).
Results showed that ID was observed in 54% of the evaluated participants and suggested that psychiatric disorders were typically diagnosed late. We also identified significant non-reported or missing data, which suggested that even in expert TSC centres around the globe, TAND may be underdiagnosed and therefore under-treated  [28]. Here, we performed a detailed exploration of the largest TAND dataset to date using TOSCA baseline data of all the patients enrolled at the cut-off of September 30, 2015, with the specific aim of comparing childhood and adult TAND profiles, describing the age-related pattern of TAND, and examining TAND in relation to genotype.
The TOSCA registry was designed and conducted in accordance with the Guidelines for Good Clinical Practice and ethical principles outlined in the Declaration of Helsinki  [29, 30]. Written informed consent, with prior endorsement by all local institutional review boards (human research ethics committees) was obtained from all participants, parents, or guardians prior to registry enrollment. For the purposes of this manuscript, descriptive statistics were used to summarize TAND data. Frequency of TAND features were extracted and presented as percentage of individuals with available data (excluding non-reported or missing data). Intellectual ability was categorized as normal (IQ > 70), mild ID (IQ 51-70), moderate ID (IQ 36-50), severe ID (IQ  [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] , and profound ID (IQ < 20), according to DSM-5/ICD-10  [31, 32].
Psychiatric disorders were defined according to the DSM-5/ICD-10. Age-based patterns of TAND (children vs adults) and the association between TAND and genotype (TSC1 vs TSC2) were analyzed using Chi-square test. The TOSCA study included data collection on all levels of TAND as outlined above and summarised in the primary TAND manuscript  [5] , apart from psychosocial characteristics, such as self-esteem, sibling, or family stressors, which were only included after the TOSCA database was set up.
Results A total of 2216 participants (1154 females and 1062 males) with TSC were enrolled into the TOSCA registry from 170 sites across 31 countries at the data cut-off for the third interim analysis (September 30, 2015). The median age of the TOSCA cohort was 13 years (range, < 1-71) with 1410 individuals (63. 6%) aged ≤18 years.
Discussion The TOSCA registry allowed exploration of the frequency of a wide range of TAND features in the largest cohort of TSC reported to date. We set out to examine the overall TAND profile, to compare childhood and adult patterns, age-based patterns, and genotype-TAND correlations. Results showed lower rates of behavioural and psychiatric disorders than previously reported, but similar rates of ID  [3, 6, 8, 12]. The very high rates of non-reported or missing data in this study (in excess of 60% for behavioural and intellectual levels) may, at least in part, have contributed to the lower rates observed. The rates of academic difficulties and neuropsychological deficits, reported in this study for the first time, were very high and suggested that between half and two-thirds of individuals with TSC will have difficulties in these two TAND levels.
We acknowledge that a TSC1 vs TSC2 differentiation may be highly oversimplistic, given that specific TSC mutations may be associated with very different functional consequences at a biochemical level  [16, 26]. The challenge around unreported and missing TAND data in this study was significant. Fewer than 40% of behavioural data were available and only 39. 9% of participants in the TOSCA registry received an evaluation of their intellectual ability. The overall rates of ID were consistent with previous studies,  [8, 12]  but we did not observe a bimodal distribution of intellectual ability as previously reported  [23, 25, 39, 40]. Given that only about half of the TOSCA cohort received a formal evaluation for IQ, it is possible that more severely impaired individuals may not have been referred for an assessment of their intellectual or developmental quotient (as was done in the population-based study that observed the clear bimodal pattern)  [39].
Academic data were not reported or were missing in 43. 4% of the cohort and 38. 9% were not reported or were missing regarding neuropsychological skills. Only 48. 8% of those with academic difficulties ever received evaluations, and only 41. 6% were evaluated for neuropsychological deficits. Whilst the very high rates of academic and neuropsychological deficits in TSC were a novel finding in the TOSCA registry and underlines the recommendation that all children with TSC should receive evaluation of their academic and neuropsychological needs,  [2, 35]  the very low proportion assessed raises significant clinical concern.
Conclusions Together, findings from the TOSCA registry emphasize the magnitude of neuropsychiatric disorders in TSC, which has an enormous impact on quality of life of individuals with TSC and their families. However, there was a high proportion of non-reported or missing data, which may have impacted the overall findings of the study. Due to the observational nature of the registry, only data already available from clinical practice were collected. Moreover, considering the disease complexity and the fact that individuals with TSC were not always followed for all disease manifestations by the site involved in the registry, ensuring report of all disease manifestations of each participant was a major challenge. We therefore acknowledge the potential ascertainment biases that may come with large-scale clinic-based natural history studies such as this one.
There is clearly a highly dynamic interaction between development, genotype, intellectual ability, epilepsy, anti-epilepsy and other pharmacological treatments, and environment in TSC. Future studies should therefore aim to examine these interrelations in an integrated way using suitable multilevel computational modelling. We also acknowledge that further evaluations will be required to examine TAND in relation to intellectual level and sex. In addition, efforts are underway to determine if there may be "natural TAND clusters" consisting of natural groupings of TAND characteristics within and between individuals with TSC across levels of TAND  [7, 13]. The TOSCA cohort may provide a powerful dataset to examine this possibility further. In spite of the limitations inherent to a large-scale natural history study, results provide strong data to encourage clinicians to evaluate for neuropsychiatric comorbidity in all children and adults with TSC.
Acknowledgements We thank patients and their families, investigators, and staff from all the participating sites. We thank  Manojkumar Patel ,  Novartis Healthcare Pvt. Ltd. for providing medical editorial assistance with this manuscript. TOSCA Investigators Japan:  Nobuo Shinohara ,  Shigeo Horie ,  Masaya Kubota ,  Jun Tohyama ,  Katsumi Imai ,  Mari Kaneda ,  Hideo Kaneko ,  Yasushi Uchida ,  Tomoko Kirino ,  Shoichi Endo ,  Yoshikazu Inoue ,  Katsuhisa Uruno ; Turkey:  Ayse Serdaroglu ,  Zuhal Yapici ,  Banu Anlar ,  Sakir Altunbasak ; Russia:  Olga Lvova ,  Oleg Valeryevich Belyaev ,  Oleg Agranovich ,  Elena Vladislavovna Levitina ,  Yulia Vladimirovna Maksimova ,  Antonina Karas ; China:  Yuwu Jiang ,  Liping Zou ,  Kaifeng Xu ,  Yushi Zhang ,  Guoming Luan ,  Yuqin Zhang ,  Yi Wang ,  Meiling Jin ,  Dingwei Ye ,  Weiping Liao ,  Liemin Zhou ,  Jie Liu ,  Jianxiang Liao ,  Bo YAN ,  Yanchun Deng ,  Li Jiang ,  Zhisheng Liu ,  Shaoping Huang ,  Hua Li ;  Korea: Kijoong Kim ;  Taiwan: Pei-Lung Chen ,  Hsiu-Fen Lee ,  Jeng-Dau Tsai ,  Ching-Shiang Chi ,  Chao-Ching Huang ; Australia:  Kate Riney ,  Deborah Yates ,  Patrick Kwan ; Thailand:  Surachai Likasitwattanakul ,  Charcrin Nabangchang ,  Lunliya Thampratankul Krisnachai Chomtho ,  Kamornwan Katanyuwong ,  Somjit Sriudomkajorn ; South Africa: Jo Wilmshurst; Israel:  Reeval Segel ,  Tal Gilboa ,  Michal Tzadok ,  Aviva Fattal-Valevski ; Greece:  Panagiotis Papathanasopoulos ,  Antigone Syrigou Papavasiliou ,  Stylianos Giannakodimos ,  Stylianos Gatzonis ,  Evangelos Pavlou ,  Meropi Tzoufi ; Netherlands:  A.
, 2013; Sippy et al. , 2015; Yamashita and Petersen, 2016). Ambient white noise (80 dB) was played continuously to mask any potential auditory cue generated by the magnetic pulse or external noise that could distract the mice. Behavioral control and behavioral data collection were carried out with custom-written computer routines using a National Instruments board interfaced through LabView or MATLAB (MathWorks). For the detection task (Figure  S2B ), trials with whisker stimulation (Stimulus trials) or those without whisker stimulation (Catch trials) were started without any preceding cues, at random inter-trial intervals ranging from 6 to 12 s. Catch trials were randomly interleaved with Stimulus trials, with 50% probability of all trials. If the mouse licked in the 3-4 s no-lick window preceding the time when the trial was supposed to occur, then the trial was aborted (Figure  S2B ).
Silicon probe recordings Extracellular spikes in the mPFC were recorded using silicon probes (A1x32-Poly2-10mm-50 s-177, NeuroNexus, MI, USA) with 32 recording sites along a single shank covering 775 mm in depth. The probe was lowered gradually with a 10 angle relative to vertical, until the tip reached a depth of $2300 mm under the surface of the pia. The probe was coated with DiI (1,1'-dioctadecyl-3,3,3 0 3'-tetramethylindocarbocyanine perchlorate, Invitrogen, USA) for post hoc recovery of the recording location (Figure  S1C ). The signals were filtered between 0. 3 Hz and 7. 5 kHz and amplified using a digital headstage (CerePlex M32, Blackrock Microsystems, UT, USA). The headstage digitized the data with a sampling frequency of 30 kHz. The digitized signal was transferred to our data acquisition system (CerePlex Direct, Blackrock Microsystems, UT, USA) and stored on an internal HDD of the host PC for offline analysis.
LFP database and analysis For multisite LFP experiments a database of recordings was made after selection of the recordings based on anatomical verification and the absence of electrical artifacts. From the 14 mice recorded during the detection task, we selected: 11 recordings in wS1, 11 recordings in wS2, 6 recordings in wM1, 11 recordings in PtA, 9 recordings in dCA1 and 10 recordings in mPFC. From the 12 mice recorded during the neutral exposure, we selected: 8 recordings in wS1, 11 recordings in wS2, 5 recordings in wM1, 7 recordings in PtA, 8 recordings in dCA1 and 10 recordings in mPFC. In order to compute the average sensory evoked potentials, the LFPs were band pass filtered between 0. 1 and 100 Hz after removing the stimulus artifact elicited by the magnetic pulse. Sensory evoked potentials were then averaged for the different conditions.
Spike sorting and analysis For silicon probe recordings, the spiking activity on each probe was detected and sorted into different clusters using the KlustaSuite  (Rossant et al. , 2016). After an automated clustering step, clusters were manually sorted and refined. Only well isolated single units were included in the dataset. The isolated units were classified as Fast-Spiking Units (FSUs, putative inhibitory interneurons) and Regular-Spiking Units (RSUs, putative pyramidal cells) based on the duration of the spike waveforms (peak to return-to-baseline time) (Figure  S1D ). Receiver Operating Characteristic (ROC) Analysis ROC analysis was performed from LFP data using the ''perfcurve'' function from MATLAB. ROC curves were built by comparing the distribution of the mean amplitude of the LFP on a 10 ms time-window for Stimulus and Catch trials.
ACKNOWLEDGMENTS We thank  L. Fernandez ,  M. Perier , and  A. Oryshchuk  for technical assistance and help with the experiments. This work was supported by  INSERM  and  University Claude Bernard -Lyon1  and funded by grants from  Agence Nationale de la Recherche  (Project  AWAKE   CX ,  ANR-09-JCJC-0028-01 , 2009),  Lyon Neuroscience Research Center  (Projet Structurant, 2009), the  Swiss National.
Results from the second iteration, in 2017, verified that the anomalies in the experiment's first iteration were due to errors in either coding or methodology, or both. One organization implemented a third trial to fix software errors. This emphasizes the importance of fully understanding both methodology and implementation, in order to ensure accurate and repeatable results. To extend this comparison study, a reference data set, which is composed of edge images and corresponding MTF curves, will be built. A broader audience will be able to access the edge images through the CEOS CalVal Portal (http://calvalportal. ceos. org/. This paper, which is associated with the reference data set, can serve as a new tool to either implement or check, or both, the MTF measurement that relies on the slanted edge method.
One of the first efforts of this group has been to compare processing methodologies for the edge method of MTF estimation. For this comparison experiment, the team was composed of Frans van den Bergh from CSIR, Renaud Fraisse from Airbus DS, Dennis Helder from SDSU, Dong Han Lee from KARI, Amy Newbury and Robert Kudola from Digital Globe, Sébastien Saunier from Telespazio and Françoise Viallefont-Robinet from ONERA This paper presents the method and its various implementations followed by the comparison experiment. The first results, obtained with a blind test approach, were analyzed. This exercise was an opportunity to correct or improve the software of each participant. Thus, a second run was performed by most of the participants in order to improve the results, leading to a second comparison.
We could then characterise the sensory response properties of these cells by monitoring changes in GCaMP fluorescence while animals were presented with a panel of eight odour stimuli (see Materials and methods; Figure  9A ;  Kapoor et al. , 2016). Given the current lack of a reliable in vivo live AIS marker, we classified DA neurons in these experiments using the proxy measure of soma size -this can be readily measured in live neurons, and is consistently associated with AIS-positive or AIS-negative identity across multiple datasets (Figures 1, 3 and 8). Our upper bound for cells classed as 'small' was 70 mm 2 , taken from the mean soma size of confirmed AIS-positive DA cells (Figure  1B , magenta distribution) minus two standard deviations (i. e. 136. 7-2 Â 33. 2 mm 2 ). Under an assumption of normality, this cutoff excludes all but the smallest 2.
13, p<0. 0001; late excitatory vs inhibitory, r = 0. 030, p=0. 30; n = 1216 in all cases), suggesting that TI values for early excitatory, late excitatory and inhibitory events represent rather independent measures of tuning for distinct types of response produced by glomerular layer circuitry. To compare these TI measures between cell types, we needed powerful statistical tests that could leverage the large numbers of imaged neurons in our dataset whilst accounting for significant across-animal variability (see Materials and methods; Figure  10B, C ). We therefore employed multilevel ANOVA analyses, where TI values from individual cells were compared between small versus big cell populations nested in animal subjects (see Materials and methods;  [Aarts et al. , 2014] ). Using this approach, we found no effect of cell type on early excitatory TI values (Figure  10B ; fixed effect of cell-type in multilevel ANOVA, F 1,1180 = 2.
Any discrepancies in classification were resolved by mutual agreement. We also corroborated our subjective classification using a quantitative measure of spike onset sharpness: the ratio of errors produced by linear and exponential fits to the perithreshold portion of the phase plane plot  (Baranauskas et al. , 2010; Volgushev et al. , 2008). Fit error ratios were calculated with a custom Matlab script written by Maxim Volgushev, using variable initial portions of the phase plane plot between voltage threshold and 40% of maximum dV/dt  (Baranauskas et al. , 2010) , for single spikes fired in response to 10 ms current injection at current threshold and up to three subsequent suprathreshold sweeps. Using strict, established  (Baranauskas et al. , 2010) , but non-inclusive criteria for 'steep' ( » biphasic; maximum fit error ratio >3) versus 'smooth' ( » monophasic; maximum fit error ratio <1) spike onset, we were able to objectively classify phase plane plot shape in a smaller subset of our recorded DAT-tdT+ neurons.
Of 13 neurons classified in this manner, only one was classified differently by subjective vs objective criteria. Importantly, the limited subset of objectively classified cells still displayed significant differences between mono-and biphasic OB dopaminergic neurons: as in the larger, subjectively-classified dataset, biphasic cells had bigger soma area, and were more excitable than their monophasic counterparts (Figure  8 -figure supplement 1). For quantification of AP properties, voltage threshold was taken as the potential at which dV/dt first passed 10 V/s. Onset rapidness was taken from the slope of a linear fit to the phase plane plot at voltage threshold. Spike width was measured at the midpoint between voltage threshold and maximum voltage. Rheobase and afterhyperpolarisation values were both measured from responses to 500 ms current injection, the latter from the local voltage minimum after the first spike fired at rheobase.