dataesr/scientific-paragraphs-categorization huggingface dataset:
The errors appear due to transcription changes of names or missing cases in our name-database based on English Wikipedia. For Western languages the number of errors is presumably reduced since transcription remains close to English. Based on the manual inspection for the Korean and the Russian Wikipedia, we expect that the errors of our automatic recovery of the top people from the whole articles ordered by PageRank and 2DRank are on a level of two percent. We also note that our study is in compliance with Wikipedia's Terms and Conditions.
Korea appearing not only in the Korean but also in the Japanese Wikipedia. In the case of the top 2DRank historical figures shown in Fig. 3B  and Fig. 3D , we observe overall patterns of locality and regions being similar to the case of PageRank, but the locality is stronger. In short, we observed that most of the top historical figures in Wikipedia were born in Western countries, but also that each edition shows its own preference to the historical figures born in countries which are closely related to the corresponding language edition.
Temporal distribution The analysis of the temporal distribution of top historical figures is done based on their birth dates. As shown in Fig. 4A  for PageRank, most of historical figures were born after the 17th century on average, which shows similar pattern with world population growth  [40]. However, there are some distinctive peaks around BC 5th century and BC 1st century for the case of PageRank because of Greek scholars (Socrates, Plato, and Herodotus), Roman politicians (Julius Caesar, Augustus) and Christianity leaders (Jesus, Paul the Apostle, and Mary (mother of Jesus)). We also observe that the Arabic and the Persian Wikipedia have more historical figures than Western language Wikipedia editions from AD 6th century to AD 12th century. For the case of 2DRank in Fig. 4B , there is only one small peak around BC 1C, which is also smaller than the peak in the case of PageRank, and all the distribution is dominated by a strong growth on the 20th century.
For PageRank persons and AD 20th century, we find that the English edition has the largest fraction of its own language, followed by Arabic and Persian editions while other editions have significantly large connections with other cultures. For the English edition this is related to a significant number of USA presidents appearing in the top 100 list (see  [18, 19] ). For 2DRank persons the largest fractions were found for Greek, Arabic, Chinese and Japanese cultures. These data show that even in age of globalization there is a significant dominance of local historical figures for certain cultures.
According to this definition, a historical figure who appears more often in the lists of top historical figures for the given 24 Wikipedia editions or has higher ranking in the lists gets a higher ranking score. Table  4  represents the top 10 global historical figures for PageRank and 2DRank. Carl Linnaeus is the 1st global historical figure by PageRank followed by Jesus, Aristotle. Adolf Hitler is the 1st global historical figure by 2DRank followed by Michael Jackson, Madonna (entertainer). On the other hand, the lists of the top 10 local historical figures ordered by our ranking score for each language are represented in SI Tables S1-S25 and  [39]. The reason for a somewhat unexpected PageRank leader Carl Linnaeus is related to the fact that he laid the foundations for the modern biological naming scheme so that plenty of articles about animals, insects and plants point to the Wikipedia article about him, which strongly increases the PageRank probability.
This happens for all 24 languages where Carl Linnaeus always appears on high positions since articles about animals and plants are an important fraction of Wikipedia. Even if in a given language the top persons are often politicians (e. g. Napoleon, Barak Obama at K = 1, 2 in EN), these politicians have mainly local importance and are not highly ranked in other languages (e. g. in ZH Carl Linnaeus is at K = 1, Napoleon at K = 3 and Barak Obama is at K = 24). As a result when the global contribution is counted over all 24 languages Carl Linnaeus appears on the top PageRank position. Our analysis suggests that there might be three groups of historical figures. Fig. 7  shows these three groups of top PageRank historical figures in Wikipedia: (i) global historical figures who appear in most of Wikipedia editions (N A ≥ 18) and are highly ranked ( K ≤ 50) for each Wikipedia such as Carl Linnaeus, Plato, Jesus, and Napoleon (Right-Top of the Fig.
We note that the Pantheon method is significantly based on a number of page views while our approach is based on the network structure of the whole Wikipedia network. The top 100 persons from  [22]  are not publicly available but nevertheless we present the overlaps between the top 100 persons from the lists of Hart, Pantheon, Stony-Brook and our global PageRank and 2DRank lists in SI Figs. S2, S3  (we received the Stony-Brook list as a private message from the authors of  [22] ). We have an average overlap between the 4 methods on a level of 40 percent (2DRank is on average lower by a few percent), we find a larger overlap between our PageRank list and the Stony-Brook list since the Stony-Brook method, applied only for the English Wikipedia, is significantly based on PageRank. We also compared the distributions of our global top 100 persons of PageRank and 2DRank with the distribution of Hart's top 100 over centuries and over 24 languages with the additional WR category (see Fig.
S4  in SI). We find that these 3 distributions have very similar shapes. Thus the largest number of persons appears in centuries AD 18th, 19th, 20th for the 3 distributions. Among languages, the main peaks for the 3 distributions appear for EN, DE, IT, EL, AR, ZH. The deviations from Hart's distribution are larger for the 2DRank list. Thus the comparison of distributions over centuries and languages shows that the PageRank list has not only a strong overlap with the Hart list in the number of persons but that they also have very similar statistical distributions of the top 100 persons over centuries and languages. The overlap of the top 100 global persons found here with the previous study  [21]  gives 54 and 47 percent for PageRank and 2DRank lists, respectively. However, we note that the global list in  [21]  was obtained from the top 30 persons in each edition while here we use the top 100 persons.
4 percent of cases which can be discussed and where a native speaking language can be a better indicator of belonging to a given culture. For the global 2DRank list of 1616 names we identified 53 similar cases where an attribution to a culture via a native language or a birth place could be discussed (about 3. 3 percent). These 53 names are given in SI. About half of such cases are linked with birth places in ancient Russian Empire where people from Belarus, Litvania and Ukraine moved to RU, IL, PL, WR. However, the percentage of such cases is small and the corresponding errors also remain small. Based on the above assumption and following the approach developed in  [21] , we construct two weighted networks of cultures (or language groups) based on the top PageRank historical figures and top 2DRank historical figures respectively.
Each culture (i. e. language) is represented as a node of the network, and the weight of a directed link from culture A to culture B is given by the number of historical figures belonging to culture B (e. g. French) appearing in the list of top 100 historical figures for a given culture A (e. g. English). The persons in a given edition, belonging to the language of the edition, are not taken into account since they do not create links between cultures. In Table  6  we give the number of such persons for each language. This table also gives the number of persons of a given language among the top 100 persons of the global PageRank and 2DRank listings. For example, there are 5 French historical figures among the top 100 PageRank historical figures of the English Wikipedia, so we can assign weight 5 to the link from English to French.
Fig. 8A  and Fig. 8B  represent the constructed networks of cultures defined by appearances of the top PageRank historical figures and top 2DRank historical figures, respectively. In total we have two networks with 25 nodes which include our 24 editions and an additional node WR for all the other world cultures. The Google matrix G ij for each network is constructed following the standard rules described in  [21]  and in the Methods Section. In a standard way we determine the PageRank index K and the CheiRank index K * that order all cultures according to decreasing PageRank and CheiRank probabilities (see Methods and Fig. S5. in SI). The structure of matrix elements G KK ′ is shown in Fig. 9. To identify which cultures (or language groups) are more influential than others, we calculated PageRank and CheiRank of the constructed networks of cultures by considering link weights.
Briefly speaking, a culture has high PageRank (CheiRank) if it has many ingoing (outgoing) links from (to) other cultures (see Methods). The distribution of cultures on a PageRank-CheiRank plane is shown in Fig. 10. In both cases of PageRank and 2DRank historical figures, historical figures of English culture (i. e. born in English language spoken countries) are the most influential (highest PageRank) and German culture is the second one (Fig. 10A, B ). Here we consider the historical figures for the whole range of centuries. Fig. 10  represents the detailed features of how each culture is located on the plane of PageRank ranking K and CheiRank ranking K * based on the top PageRank historical figures (Fig. 10A ) and top 2DRank historical figures (Fig. 10B ). Here K indicates the ranking of a given culture ordered by how many of its own top historical figures appear in other Wikipedia editions, and K * indicates the ranking of a given culture according to how many of the top historical figures in the considered culture are from other cultures.
Head and forward models The head model used here was based on a segmentation of the MNI152 template brain and the leadfield and forward solution were computed with the OpenMEEG software  (Gramfort et al. , 2010; Kybic et al. , 2005) , which is based on a symmetric Boundary Element Method. Four nested layers were modeled (Fig. 2A-D ): the brain envelope (smoothed outer part of the brain), the cerebro-spinal fluid (CSF), the outer skull and the scalp. The conductivity of air was set to 0. The other conductivities are relative, and the conductivity of the skull was set to. 03 of the brain, while the CSF conductivity was set to 3 (3 times the brain and skin one, both set to 1). Sixty-four electrodes were modeled, located on the standard extended 10-20 system positions (Fig. 2E-F ).
Data processing The time courses of the reconstructed scalp potential on each electrode and for each reference electrode were analyzed as one would do with real EEG measures. In a second step, the data were SL transformed. This was done following  Perrin et al. 's (1987 Perrin et al. 's ( , 1989) )  method, as implemented in the CSD toolbox  (Kayser and Tenke, 2006b). Note, however, that we used a re-implementation of the algorithm in Python. The order of spline used was set to 3 (m parameter in  Kayser and Tenke, 2006b) , and the smoothing constant was set to 10 -5 (λ parameter). From the potential, the SL was computed on all electrodes and at all time points. For the sake of simplicity, analyses were focused on the central electrodes (C1, Cz and C2), above the simulated sources. The latencies of the peak of activity for these electrodes were extracted for each simulation parameter, for both scalp potentials and SL transformed data.
Relatedly, while the amplitude of the potential obtained at electrode Cz was comparable to C1 and C2 electrodes (see green trace in Fig. 3 ), this amplitude is dramatically decreased after SL computation (see green trace in Fig. 4 ). Second, the timing of the CSD activities also largely differs: the first and last dashed lines, indicating dipole peaks, are now aligned with the peak of the CSD activities on C1 and C2 (colored arrows), indicating that the CSD activities peak approximately at the latencies of the underlying simulated sources. To visualize how scalp potentials and CSD data differ, we extracted the latency of the peak of activity on C1 and C2 electrodes for the different simulated time courses. Fig. 5B  shows the actual dipole latencies (solid lines) and the recovered latencies (open symbols) at scalp level, for each reference electrode configuration.
Dataset # 1: deciding and acting The first dataset comes from  Vidal et al. (2003)  and concerns cortical processes involved in response selection and execution. This study was interested in the functional organization of the pre-motor (mainly the pre-supplementary motor area, pre-SMA) and motor (mainly primary motor areas) areas in selecting and executing a response in bimanual choice situation. 3 The source derivation method and the spline interpolation approach have been shown to provide highly comparable results  (Tandonnet et al. , 2005; Tenke et al. , 1998).
Stimuli and task All details about this dataset can be found in  Vidal et al. (2003). Only the relevant information will be described here. Participants performed a manual Stroop task, in which they had to respond with a right or a left hand key-press as a function of the color of a written word. Responses were given by thumb presses, and electromyographic (EMG) activities of the two flexor pollicis brevis were measured. Scalp potentials were referenced to the left mastoid. After careful artifact rejection, the data were averaged time-locked to EMG onset. EEG was recorded with 21 scalp electrodes, positioned so that the SL could be estimated by the source derivation method  (Hjorth, 1975) , as modified by  MacKay (1983). With such method, the SL at electrode O is computed as [3V O -(V A + V B + V C )]/d 2 where V O , V A , V B and V C represent the potential recorded at electrodes O, A, B and C, provided that electrode O is at the barycenter of the triangle ABC, at a distance d of each vertex.
Discussion As in the simulation study, scalp potentials recorded in this experiment show similar time courses for both FCz and C3 electrodes (about 7 cm apart). After CSD computation, however, a different pattern is observed: the activity of the two electrodes is separated temporally, revealing two cortical generators, activated sequentially. Thus, as in the previous simulations, the presented data show that volume conduction effects hinder timing differences between brain regions. Applying SL transform to the data reveals much larger temporal differences which, according to the simulations above, likely reflect true underlying brain activity differences. From a functional point of view, the interpretation based on SL supports a sequential involvement of the (pre)SMA and M1 in the selection and execution of response  (Vidal et al.
, 2003 (Vidal et al. , , 2011;; Burle et al. , 2004) , at best scalp potentials do not allow to reach this conclusion, and at worse, one may conclude, based on the similar time courses, that the underlying cortical areas work in parallel. In the present dataset, CSD was approximated by the source derivation method  (Hjorth, 1975; MacKay, 1983). Since this original publication  (Vidal et al. , 2003) , this sequential activation has been replicated several times with both Hjorth method (e. g. Vidal et al. , 2011)  and with the spline interpolation one (see e. g. Carbonnell et al. , 2013). In some of those studies, these two activities were shown to be independently modulated by different factors, which confirm that they reflect the activation of independent cortical generators. Note that the separation of such activities, providing powerful markers of response selection and execution, was never possible on the scalp potential data.
EEG acquisition EEG was acquired with 64 active-2 electrodes (Biosemi, Amsterdam) located at the standard extended 10-20 system. All electrodes were off-line referenced to the left mastoid. After ocular artifact correction  (Gratton et al. , 1983) , all the signals were carefully inspected to remove all other artifacts. Great care was taken to remove local artifacts, since The time courses are pretty similar between the two electrodes, and mainly present a large and temporally spread positivity that peaks slightly after EMG onset. A small bump is observed around -50 ms, with a similar time course for both electrodes. B. Same data after SL computation: The recovered activities dramatically differ between the two electrodes. Over electrode FCz, a negative peak is observed around 40 ms before, and resolves shortly after, EMG onset.
An apparent change in latencies does hence not necessarily reflect a true chronometric difference. Again, CSD estimation removes this ambiguity as the recovered time courses are not (or at least much less) affected by remote sources, and are hence not biased toward the largest source. In the simulations, realistic head model (geometries, conductivities etc. ) was used. However, for the sake of clarity and to better illustrate the volume conduction and reference electrode effects, we restricted the simulations to only two dipoles, with very simple (i. e. very smooth) time courses. To generalize the results, similar effects were then shown on two empirical data sets: one related to response selection and execution, and the second one related to visual information processing. Those two datasets were also chosen because they are based on two different methods to compute SL, hence evidencing the robustness of the method.
In both datasets, while scalp potentials presented very similar activities (in terms of shape, timing etc. ) on different electrodes (up to 7 cm), the SL data provide a very different view: CSD activities differ dramatically, even at close-by electrodes (Oz and O1/O2 are separated by only 2. 5 cm), with very different time courses and morphologies. In both cases, SL allowed to reveal specific activities that were not suspectable on the scalp potentials. C. Summary of the peak latencies (y-axis) for the representative electrodes (x-axis) for scalp potentials (colored squares) and CSD (colored circles). While the latency differences are rather small for scalp potential data (max difference: 38 ms), the CSD evidences a clear occipito-parietal latency gradient (max difference: 81 ms). In the first empirical case described above (Section 3.
CSD Potentials The second dataset evidences similar type of effect on visual evoked potential. Scalp potentials have a very similar shape and timing over different occipito-parietal electrodes. Indeed, all (represented) electrodes first present an early positivity around 100 ms, followed by a negativity around 170-200 ms and a later large positivity after 300 ms. While the timing of the negative peak varies slightly across electrodes (less than 40 ms, see Fig. 9C ), the scalp potentials appear largely driven by the (rather) late occipito-parietal activities evidenced on electrodes PO7/PO8 after CSD (green trace in Fig. 9B ). As a consequence, the latencies observed on more medial and more posterior electrodes (e. g. Oz, O1/O2) are overestimated, being attracted by the large occipitoparietal one.
For about a quarter of the glass tubes, this treatment resulted in holder moments below about 10 -9 emu (10 -12 Am 2 = 1 pAm 2 ). On occasion they could become magnetically 'invisible', consistently reaching down to the background noise level of the DC SQUID sensors. In addition, we implemented a routine in the RAPID software that allows the empty sample holder to be cleaned with maximum strength alternating-field (AF) demagnetization after every set of nine measurements. To place this in context, we note that our standard specimen size (cylinders ~2. 54 cm in diameter, by ~1 cm high) is about 40% smaller than the standard sample size traditionally used in most paleomagnetic laboratories (e. g. ,  (Butler, 1992) ). We did this because the smaller specimen volume allows twice as many samples to be put into the magnetically-shielded furnaces for thermal demagnetization (up to ~80 specimens per load), doubling the measurement rate.
, 2008). Between 15 and 30 discrete demagnetization experiments were performed per specimen (average 24), on over 320 specimens, for an overall data set of over 7800 complete measurements of NRM. Principle magnetic components were determined using the techniques of  Kirschvink (1980) , with the parameter for the Maximum Angular Deviation (MAD) set at 15°. Due to the weak nature of the NRMs, components that were trending toward the origin, or formed 'stable endpoint clusters' before the onset of unstable behavior, were fit by forcing the least-squares line through the origin (anchoring).
Middle-Upper Permian magnetostratigraphy and bio-and chemostratigraphic correlation Our data reveal multiple geomagnetic polarity changes within the Guadalupian Iwato Formation primarily from low-latitude mid-Panthalassa. As summarized in Fig. 6 , the lower half (Wordian part) of the Iwato Formation is dominated by reversely magnetized intervals, whereas the upper half (Capitanian part) by normal ones. In contrast, the lower part of the overlying Mitai Formation above the G-LB recorded a stable reversed interval. This overall aspect is in general agreement with previous compilations of Permian magnetostratigraphy (e. g. ,  (Gradstein et al. , 2012; Steiner, 2006) ). It is particularly noteworthy that a short normal interval appears around the G-LB (Fig. 6 ). This signal is possibly correlated with the magnetostratigraphic record from South China that is characterized by a short-term normal interval for the Clarkina postbitteri honshuiensis Zone that defines the topmost Capitanian at the GSSP of G-LB in South China  (Shen et al.
Although MUSE is a general purpose instrument and has a wide range of applications (see  Bacon et al. 2014  for a few illustrations), it has been, from the very start of the project in 2001, designed and optimised for performing deep field observations. Some preliminary measurements performed during the first commissioning runs had convinced us that MUSE was able to reach its combination of high throughput and excellent image quality. However it is only by performing a very long integration on a deep field that one can assess the ultimate performance of the instrument. This was therefore one key goal of the final commis-sioning run of ten nights in dark time late July 2014. The Hubble Deep Field South (HDFS) which was observable during the second half of the nights for a few hours, although at relatively high airmass, was selected as the ideal target to validate the performance of MUSE, the observing strategy required for deep fields to limit systematic uncertainties and to test the data reduction software.
This variance data cube is saved as an additional extension of the combined data cube. In addition an exposure map data cube which counts the number of exposures used for the combination of each voxel was also saved. 9. Telluric absorption from H 2 O and O 2 molecules was fitted to the spectrum of a white dwarf found in the field (α = 22h32 58. 77 , δ = -60 o 33 23. 52 ) using the molecfit software described in Smette et al, submitted. and Kausch et al, submitted. In the fitting process, the line spread function was adjusted using a wavelength dependent Gaussian kernel. The resulting transmission correction was then globally applied to the final datacube and variance estimation. The result of this process is a fully calibrated data cube of 3 Gb size with spectra in the first extension and the variance estimate in the second extension, as well as an exposure cube of 1.