dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Y. Chen ,  K. Mori ,  G. DelSal ,  B. Viollet ,  H. Louvel ,  E. Greotti ,  A. DeMatteis ,  R. Venditti ,  A. Hausser ,  R. Rizzuto ,  D. Vecellio Reane ,  L. Scorrano ,  L. Pernas ,  C. Cheng ,  E. Melloni ,  M. Pende ,  R. Talha ,  R. Ogawa ,  J. Goldstein P. Espenshade  and  W. Shao  for protocols and materials,  S. Giulitti  for generous help with hydrogels,  M. Pellegrini  and  I. Zorzan  for help with hPSC cultures and  G. Martello  and  M. Montagner  for thoughtful discussions. This work was supported by  AIRC   IG-15307  and  IG-21392 ,  WCR 15-1192 ,  CARIPARO Eccellenza 2017  and  University of Padua BIRD  grants to S. D. , an  AIRC Hard ROCK Café Fellowship  to G. S. , the  UPMC 'Interface pour le Vivant ' doctoral programme to S. M. and an  AIRC Special Program Molecular Clinical Oncology '5 per mille '  10016  to S.
e-g, qPCR for established SREBP targets in MCF10ATk1 (e), RPE1 (f) or 3T3L1 (g) cells plated on stiff (E ≈ 15 kPa) or soft (E ≈ 0. 5 kPa) ECM-coated hydrogels for 24 h (n = 4 biologically independent samples pooled across two independent experiments for each bar; multiple unpaired two-tailed Student's t-tests). In d-g, data are relative to GAPDH levels; mean expression in controls (DMSO or stiff) were set to 1, and all other samples are relative to this. Data are mean and single points. All n values are pooled between independent experiments. Source data are provided in Supplementary Table 3. Gene list enrichment analysis on probes significantly upregulated (mean fold > 1. 3, P < 0. 05) in microarrays of MCF10ATk1 cells treated with DMSO or Y27632+ ML7 (YM) for 6 h. The graphs display the 10 most significantly overrepresented gene sets for each of the indicated databases, analysed with Enrichr and ranked according to combined score (x axis).
Since HAMP MIRA data are used in retrievals of cloud microphysics, the target accuracy will be set to 1 dB. An accurate absolute calibration is further motivated by recent studies  (Protat et al. , 2009; Hennemuth et al. , 2008; Maahn and Kollias, 2012; Ewald et al. , 2015; Lonitz et al. , 2015; Myagkov et al. , 2016; Acquistapace et al. , 2017) , which used the radar reflectivity provided by almost identical ground-based versions of the same instrument. The installation of the MIRA instrument on many ground-based cloud profiling sites within ACTRIS (Aerosols, Clouds and Trace gases Research InfraStructure Network; http://www. actris. eu, last access: 10 March 2019) and in the framework of Cloudnet is a further incentive for an external calibration study. The need for an external calibration is furthermore encouraged by several studies which already found evidence of an offset in radar reflectivity when comparing different cloud radar instruments.
The 35 GHz cloud radar on HALO The cloud radar on HALO is a pulsed Ka-band, polarimetric Doppler millimeter-wavelength radar which is based on prototypes developed and described by  Bormotov et al. (2000)  and  Vavriv et al. (2004). The current system was manufactured and provided by Metek (Meteorologische Messtechnik GmbH, Elmshorn, Germany). The system design and its data processing, including an updated moment estimation and a target classification by Bauer-Pfundstein and  Görsdorf (2007) , was described in detail by  Görsdorf et al. (2015). The millimeter radar is part of the HALO Microwave Package (HAMP) which will be subsequently abbreviated as HAMP MIRA. Its standard installation in the belly pod section of HALO with its fixed nadir-pointing 1 m diameter Cassegrain antenna is described in detail by  Mech et al.
8. Furthermore, a realistic and well-tested particle size distribution (PSD) is used. Since PSDs are known to be highly variable  (Intrieri et al. , 1993) , we choose the normalized PSD approach by  Delanoë et al. (2005) , which is based on an extensive database of airborne in situ measurements. Figure  8  shows this PSD as a function of D eq for different effective ice crystal radii r eff. Following  Delanoë et al. (2014) , the effective radius is derived using the method of  Foot (1988) , which increases proportional to the ratio of mass to projected area. The mass-size relationship and PSD are also a key component of the synergistic radar-lidar retrieval DARDAR  (Delanoë et al. , 2014) , which is designated for the EarthCARE mission. In the following, "Rayleigh scattering only" will be compared to Mie scattering and T-Matrix scattering theory.
The reader is advised that the results in Fig. 9a  probably underestimate   (Delanoë et al. , 2005)  and the mass-size relationship (green curve)  (Heymsfield et al. , 2010)  are based on an extensive database of airborne in situ measurements. the backscatter for snowflakes at larger D eq  (Tyynelä et al. , 2011). In a next step, this result is integrated using the normalized PSDs for different effective radii. The results for a fixed ice water content of 1 g m -1 and variable effective ice crystal radius is shown in Fig. 9b. Lower effective reflectivity values are almost identical, while larger effective reflectivities at 94 GHz are below the values at 35 GHz. For these realistic PSDs, effective reflectivities deviate from Rayleigh theory for effective radii larger than 80 µm at 94 GHz and 120 µm at 35 GHz.
1 dB) caused the 2. 9 dB underestimation of the inherent noise power P n. This, in turn, lead to an 2. 9 dB underestimation of Z e. -Furthermore, no correction of the finite receiver bandwidth loss was applied to previous data sets of HAMP MIRA. Using the spectral response measurements, this study can now give an estimate for the finite receiver bandwidth loss L fb of 1. 2 dB. In addition, our study re-evaluated the previously assumed attenuation by the belly pod and additional waveguides with the following measurements: -The thickness of the epoxy quartz radome in the belly pod was designed with a thickness of 4. 53 mm to limit the one-way attenuation to around 0. 5 dB. Deviations during manufacturing increased the planned belly pod thickness from 4. 53 to 4. 84 mm. This increased the oneway attenuation from the initially assumed value of 0.
Acknowledgements. This work was jointly supported by the  German Aerospace Center (DLR) , the  Max Planck Society (MPI)  and the  German Research Foundation (DFG)  through the  HALO Priority Program SPP 1294 , "Atmospheric and Earth System Research with the  Research Aircraft HALO (High Altitude and Long Range Research Aircraft)". Measurement equipment was kindly provided by  DLR-HR. DLR-FX  and its pilots are thanked for their great support during research flights. GPM data were provided by the  NASA/Goddard Space Flight Center's PPS team , which develop and compute the " GPM DPR Ka vertical profile precipitation swath retrievals " (https://doi. org/10. 5067/GPM/DPR/Ka/2A/04 )as a contribution to GPM. The authors thank  Oliver Lux  for his internal review of the paper. The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association.
Data availability. The recalibrated data set of HAMP MIRA is available in Earth System Science Data with the identifier https://doi. org/10. 1594/WDCC/HALO_measurements_1, https://doi. org/10. 1594/WDCC/HALO_measurements_2, https://doi. org/10. 1594/WDCC/HALO_measurements_3, https://doi. org/10. 1594/WDCC/HALO_measurements_4. The data set is described in detail in  Konow et al. (2019) , https://doi. org/10. 5194/essd-2018-116. All data sets created during the internal calibration are provided upon request.
Several dictionary websites are available on the web to access semantic, synonymous, or spelling information about a given word. During nine years, we systematically recorded all the entered letter sequences from a French web dictionary. A total of 200 million orthographic forms were obtained allowing us to create a large-scale database of spelling errors that could inform psychological theories about spelling processes. To check the reliability of this big data methodology, we selected from this database a sample of 100 frequently misspelled words. A group of 100 French university students had to perform a spelling-to-dictation test on this list of words. The results showed a strong correlation between the two data sets on the frequencies of produced spellings (r = 0. 82). Although the distributions of spelling errors were relatively consistent across the two databases, the proportion of correct responses revealed significant differences.
Introduction Due to the exponential growth of computer capacities both in terms of storage and processing, a new area of research has emerged over the last decade, notably in the field of psychology. Frequently coined the "big data" (r)evolution  [1] , it simply reflects a new scientific situation that allows us to collect, store and access massive amounts of data that measure various aspects of human behaviors. One of the main concerns for experimental psychologists in front of these new gigantesque datasets is to determine how to use them in order to adequately inform psychological theories. The present study is precisely designed to address this issue by considering a new large-scale database of spelling performances that has been collected over years from a French dictionary website. Collecting large amounts of data from the web is no longer a problem for the very last generations of computers and for researchers studying the cognitive processes involved in written production.
These new databases can even provide strong empirical constraints for testing computational models of written word production  [2]. For example, it would be useful to know the set of possible spelling errors that can be produced for a given word and, more precisely, to have access to robust estimates of the quantitative distribution of these errors. Let us, for example, consider the English word "inaccurate" and the following spelling error "innacurate"  [3]. Can we get a good estimate of the proportion of that misspelling among all erroneous spellings related to "inaccurate" and can we provide a theoretical account for this proportion in terms of computational models and predictions generated from computer simulations? In the present study, on the basis of millions of web entries, we were able to connect most erroneous productions (i.
e. , letter strings that do not appear as words in a lexical database) to their base word and to compute estimates of the distribution of spelling errors for a large set of words. Developing new methodologies that increase the grain size of empirical data frequently help increasing the grain size of models and theories. In the related domain of visual word recognition, large-scale item-level databases have been introduced two decades ago to test the descriptive adequacy of various existing models and this approach has been particularly useful in pushing forward the precision of both data and theories  [4] [5] [6] [7] [8] [9]. However, large-scale reading studies can only be done in the lab because they require recording devices for carefully measuring the main dependent variable, i. e. , response times.
In the best cases, 30 participants produced naming times for a set of 3,000 words, leading to a dataset of 90,000 datapoints  [6]. In the case of spelling, one important limitation of lab experiments is the restricted number of items participants can process. Obtaining a database including at least 1,000 words would require a minimum of 3 hours per participant and although this spelling-to-dictation test could be done in several separated sessions, the quality of spelling patterns after one hour of experiment would certainly be affected by fatigue and boredom. Collecting spelling patterns on the Web may provide a solution to these limitations. Indeed, the scale of magnitude regarding the number of items composing the database can be far larger with no restriction on a specific set of words.
It can also rapidly be applied to several languages having different phonographic structures with no need to run the same resource consuming experiments. Large-scale databases collected on the web could then open a new era for cognitive modeling in the domain of spelling production. A critical difficulty with the data collected on the Web is that, unlike standard experimental data collected under controlled experimental conditions and following a set of precise instructions, there is virtually no information on how these spellings been produced. Although the big data methodology can provide robust-and almost noise-free-empirical estimates of spelling performances due to the huge number of collected data points, there could be qualitative differences between these large datasets and standard experimental data collected in the lab due to unexpected biases or procedural differences.
That is the reason why comparing the results of both methodologies (i. e. , big data vs. standard experimental data from the lab) should allow us to check whether spelling performances extracted from a big data methodology do display the same quantitative and qualitative properties than the experimental data generated from the lab. In the present study, spelling errors were collected in French, a language that is particularly inconsistent when one needs to retrieve the orthographic transcription of a spoken word. Let us first briefly review three of the main characteristics of the French orthography that explain why misspellings are rather frequent in this language, even in highly educated adults. First, like many other languages with an alphabetic system, French has an inconsistent one-to-one mapping between phonemes and graphemes.
For nine years, one of us (JLM) systematically recorded all the requests that were addressed by web users to the dictionary of synonyms. About 200 million orthographic forms were collected and used to create a large-scale database of spelling performances including both correct orthographic forms and errors. However, as mentioned above, it is unclear whether the same kind of spelling errors is produced with a dictionary website and in other situations such as spelling to dictation, or typing words in sentence contexts, for instance. Task constraints are probably not the same, and we cannot be sure, a priori, that spelling errors will be similar and will have the same distribution in various tasks. For example,  [19]  compared the frequencies of occurrence of 351 correct or misspelled forms obtained from a dictionary website (isolated words) and from discussion forum websites (words in sentence context) and a correlation of r = 0.
If one observes a reasonable agreement between the spelling data obtained from a dictionary website and those obtained in another situation, say in spelling to dictation, then it will be possible to generalize the observations made on automatically generated large-scale databases of spelling errors to other common situations. However, if some systematic difference appears, then we must take this difference into account when analyzing a set of spelling productions in order to adequately use these large-scale databases to model written word production processes. To address this issue, a spelling to dictation experiment was conducted on a large sample of participants (i. e. , 100) with words selected from the website database. The resulting data were compared to those obtained with the dictionary website in order to estimate the similarities and possible qualitative differences between the two datasets.
The web-dictionary database The observation corpus originated from all requests to the electronic dictionary of synonyms from CRISCO  [18]  during the first 9 years it was put on-line  (October 1998 -December 2007). This corpus corresponds to about 200 million requests, and about 4 million distinct orthographic forms were observed. Only those appearing more than 200 times were selected resulting in a set of 58. 509 orthographic forms. From this large dataset, orthographic forms could correspond either to a word or a misspelled word. The reference lexicon used to retrieve the French words corresponding to the letter strings that were typed by users was the MORPHALOU French open morphological lexicon (about 540. 000 lexical entries;  [20, 21] ). Whenever the entered letter string was found in the reference lexicon, it was classified as a correct spelling.
Materials and methods A sample of 100 words whose percentage of spelling errors in the website database varied from 3. 81% to 79. 64% (average 29. 16%) was randomly selected from the database. The number of occurrences of these words (word requests with or without spelling error) in the website database varied from 947 to 79818, and their frequency varied from 0. 07 to 236. 89 occurrences per million according to the "Lexique 3" count in books  [23]. We found a positive correlation between the number of occurrences in the database and the log-frequency of words (r = 0. 39, p<0. 0001).
Results A total of 653 distinct misspelled strings were obtained, in addition to the 100 correct words, in the database or in the experiment. 593 misspelled strings appeared in both data sets, 29 misspelled strings appearing only in the website database and 31 only in the dictation experiment. All 753 appearing strings (i. e. , 653 misspellings and 100 correct spellings) were taken into account in the analyses. For each string, a frequency of occurrence was computed for the website and the dictation databases in order to compare these databases at the item level. Concerning the website database, the frequency of each string was the ratio of its number of occurrences on the number of occurrences of all strings related to the target word (multiplied by 100). For the dictation data, a data table of 753 strings-by-100 participants was built, with the value 1 for each cell where the participant produced the string, and the value 0 otherwise.
Total = 4392 For each string, the website frequency is computed by dividing the number of occurrences obtained for that string by the total number of strings related to the target word (i. e. , 4392). The dictation frequency is simply equal to the number of participants having produced that string. Note that two strings were not present in the website database (i. e. , "allusinant" and "hallucinent") but were produced in the dictation experiment. Conversely, one string appeared in the website database (i. e. , "alucinant") and not in the dictation experiment. https://doi. org/10. 1371/journal. pone. 0226647. t001 test was done on an independent sample of 100 different participants under the same experimental conditions, estimating the amount of systematic variance should tell us the range of correlations that should be obtained between the string frequencies of these two independent groups of 100 participants.
e. , m = 753 and n = 100). As one can see in Fig  1 , there was no visible or significant difference between the theoretical model prediction from the ECVT test and the empirical correlation function, which means that we can confidently use the ICC statistic to estimate the proportion of systematic variance in the string frequencies from the dictation data. The ICC of these data is equal to 0. 9813, with a 99% confidence interval of [0. 9787, 0. 9837], that is, there is about 98% systematic variance in the vector of 753 string frequencies indicating that the dictation database is highly reliable and provides robust estimates of spelling error distributions.
Comparison between the web database and the dictation data The correlation coefficient between the production frequencies of the 753 observed strings in the database and under dictation is r = 0. 8191, that is, the two data sets share about 67. 1% item variance (if one considers that the website string frequencies are approximately noise-free due to the large number of observations). This percentage of shared variance is much less than expected from the ICC (98. 13%). Thus, it is clear that there is some systematic difference between these two data sets. The correlation between the frequencies of correct spellings was r = 0. 6175 (N = 100), while the correlation between the frequencies of spelling errors was r = 0. 7588 (N = 653). Thus it seems that the main discrepancy between the two data sets concerns the frequencies of correct spellings.
A critical step in this study was the string-matching procedure that was applied to erroneous letter strings collected from the Web Dictionary to retrieve their associated lexical entries. Applying this procedure allowed us to automatically recover a large proportion of base words (i. e. , 85. 9%). The lab experiment can also inform us about the validity of this procedure because the base words are known, by definition, in the spelling test under dictation. We therefore applied (thanks to a judicious suggestion from one of the reviewers) the same stringmatching procedure to the spelling errors collected during the dictation test. In the same way, we found that 90. 4% of these errors could be related to the correct base word by applying this automatic procedure. This result indicates that we can be confident using this procedure to retrieve the associated lexical entries, which is crucial for building the database.
Quantitative differences In the website database, the average percentage of correct word spellings is 70. 84%, while for the word dictation, the percentage of correct word spellings is 48. 03%. In order to test this difference, we built an "accuracy regressor" in the following way: a coefficient equal to 1 was associated to each correct spelling (100 strings), and a coefficient equal to -1 to each misspelling (653 strings). The correlation between the accuracy regressor and the website database string frequency was r = 0. 9305, while this correlation was only r = 0. 6763 for the dictation string frequency. These two correlations are significantly different according to Williams T 2 test  [25, 26] : T 2 (750) = -33. 7335, p<0. 0001. Thus, it is clear that the productions in the database are overall much more accurate than those obtained under dictation.
Qualitative differences To better understand the qualitative differences between these two databases, we compared the results of two regression analyses involving different sets of regressors that are known to affect spelling performances in French  [27]. In the first analysis, we tested the effect of six regressors that could be easily computed and used on both the percentage of correct spellings and the distribution of errors for both the website and the dictation databases. In the second analysis, we tested the effect of another set of 8 regressors on the percentage of correct spellings only and we compared the results of this regression analysis to the one reported in  [27]. We restricted this analysis to correct spelling because the item values for these regressors were directly available from the MANULEX database  [28] , which was not the case for errors.
Regression analysis 1 In this first regression analysis, we used lexical and sublexical variables in order to estimate the respective contribution of these factors to spelling performances. Among the 6 regressors, there were 3 lexical variables that were extracted from the Lexique 3 database  [23]  and that correspond to standard variables that are known to affect spelling performances  [27]. As mentioned above, since our goal was not to provide a full account of spelling performances, we used sublexical variables that were easily accessible, such as the number of letters in a word or bigram frequency counts, in order to obtain a first overview of the contribution of these variables to spelling performances in both databases. The 6 regressors were: • The logarithm of the target word frequency (plus 1) in books (from Lexique 3).