dataesr/scientific-paragraphs-categorization huggingface dataset:
We took a different, complementary, approach and designed a new set of ∼140 visual stimuli, constructed by scanning real 3D objects (either "natural" or realistic toy versions of "natural" objects) with a laser scanner (Figure  1 ). In this way, we obtained lists of 3D coordinates of dots depicting these objects, available as "ASCII text files" that can easily be displayed, edited and modified. Different formats are available ( *. x3d, *. wrl) together with free software that can be used for visualization. With these stimuli, simple routines permit versatile transformations that can be performed in real time (see Figure  2 ). There are, however, limitations to this approach: the rendering of objects, in its simplest format, is not realistic as it lacks contour, color and texture, as well as diagnostic features that often provide a key to object recognition.
, 1999; Knapp and Heidingsfelder, 2001; O'Neil and Penrod, 2001; Birnbaum, 2004). The second issue is that participants running online experiments are usually diverse and mostly unknown. In addition, the environmental conditions, such as lighting, display characteristics, ambient sounds, are vastly disparate and cannot be easily controlled for, and response biases induced by the design of the response page, or by subtle cues given to participants can occur. Sometimes, combining a web-based experiment with a laboratory experiment permits to control for some of these biases  (Dandurand et al. , 2008). The aim of the present work is: (i) to advertise the stimuli stored in the OD3D available database and to present the results of the normative tests conducted with this set similar to  Snodgrass and Vanderwart's (1980) ; (ii) to measure the minimal number of dots (dot threshold) needed to recognize, categorize and identify the OB3D objects which provide a quantitative measure of the minimum information needed to recognize and categorize these 3D objects.
PARTICIPANTS The experiment was promoted through mailing to the RISC ("Relais d'information sur les sciences de la cognition," www. risc. cnrs. fr) volunteers' database (http://expesciences. risc. cnrs. fr/ contenu. php). Participants ranged between 22 and 54 years old (Mean age 28. 6 years, ±7. 5; women/men ratio of 63:37), and were native French speakers. All reported having no neurological disease and having normal or corrected to normal vision. The experiment was done by visiting the experiment's website (http://cogitolabo. risc. cnrs. fr/ob3d. php) and could be performed in different sessions. Participants gave their consent before starting the experiment and were explained that they were free to stop at any time and for any reason. If they stopped early, participants received a password to reconnect to the web site and to continue the experiment where they left it, if they wished.
STIMULI 3D Objects from the OB3D database are free, open source available online (http://ob3d. scicog. fr/). The only requirement is to cite the website and to provide feedback such as data, links to articles or to new objects, etc. The 3D objects were created by scanning real life objects or "toy objects" with a Scan arm® Faro laser scanner (http://www. faro. com) allowing fast and accurate object acquisition. This hand-held laser scanner creates a 3D image through a triangulation algorithm: a laser line projected onto an object is reflected on a sensor measuring the distance to the surface, using an internal coordinate system provided by calibrated internal sensors. The scanned object lay on a flat plane known to the system such that extra points belonging to this plane are removed. Scans from different viewpoints are assembled using manually defined homologous points from different views.
The 3D clouds of X, Y, Z ASCII coordinates of each object are available in several formats [. wrl,. obj,. wrp. See reference for. wrp (wraped file) with Geomagic software tm http:// www. geomagic. com/]. Further manipulations and transformations are fairly easy, because of the complete characterization of the object. Other formats are available (Polygons, vectors), but are more resource intensive. The whole procedure is described in Figure  1. On average, 10 5 points defined each object. Figure  2  presents down-sampled (10000 dots) versions of objects from the set. With these stimuli, a large number of object transformations are possible, such as rotating objects, decreasing the number of dots, changing the size, and proportions, adding positional noise, changing color, generating scramble versions, mixing, and morphing between objects, etc.
The website was tested with all major web browsers and optimized desktop computers. Prior to the experiment proper, participants could test whether their browser supported WebGL (a JavaScript API for the rendering of interactive 3D graphics) and explained how to enable WebGL. All data were stored in a relational database, MySQL (number of dots needed to recognize the object, and the questionnaire answers). The database architecture is presented in Figure  4. The experiment follows most of the recommendations of  Birnbaum (2010) , and complies with most of the standards for internet-based experiments provided by  Reips (2002) : First, the analyses were decided before designing the web-based experiment. Second, the programming of the study was checked for bugs several times, both off-line and online.
NAMES Mean Name agreement was 62% (±48%). This result is very close to the results of  Brodeur et al. (2010). Mean H name was 1. 69 (±0. 11), also very close to the 1. 65 (±1. 10) found in the study of  Brodeur et al. (2010). In both studies, the results indicate that the participants used more alternate names to identify the objects than in previous studies [e. g. , H name between 0. 56 (±0. 53) and 1. 16 (±0. 79) in  Snodgrass and Vanderwart, 1980; Bates et al. , 2003, respectively]. This discrepancy can mainly be attributed to the selection and number of the objects in each database. A large object database will contain objects that are more difficult to name than a small one. Depending on the intended use, it can be advantageous to have samples of objects that are more difficult to name, and others that are less difficult.
DISCUSSION The present work describes a web-based experiment aimed at the normalization of a novel visual stimuli data set. This experiment was done in order to illustrate both the stimuli properties and on how valuable a web-based can be regarding database normalization. The OB3D is a free database of 3D objects that can be used by themselves, or embedded in virtual reality (VR) settings, with a comprehensive normalization. This data set is the first of its kind because one can easily customize the stimuli to fit with the experimental paradigm chosen by the researcher or clinician, and still be controlled for low-level vision cues.
Beyond their descriptive value, normative variables can reflect various kinds of cognitive processing and be related to specific brain activities. For instance, objects of different categories are known to activate selective patterns of the brain within the dorsal occipital cortex, the superior temporal sulcus, and the ventral temporal cortex. In another experiment, our stimulus set was used in an MEG experiment to draw a comparison with more traditional localizers, such as grayscale pictures. We found that the OB3D stimuli could indeed activate the Lower Occipital Complex (LOC)  (Benmussa et al. , 2012). So far, the experiment reported here has not been linked to the web experiment list (http://www. wexlist. net/) mainly because the experiment was limited to a specific sample of participants drawn from the RISC database.
VR is at the same time technology, communication interface, and compelling experience. Because of population aging, and global economy uncertainty, free tools, such as tests, software (e. g. , NeuroVR 2,  Riva et al. , 2010) , and databases, may be key contributions to lower the overall costs and to encourage the patients to contribute by themselves, in a new way of empowerment (see http://www. patientslikeme. com/). New trends are already emerging in patients' contribution through the internet  (Wicks et al. , 2014).
CONCLUSION We performed a web-based experiment aiming at normalizing a novel visual stimulus database made of 3D scans of "natural" objects. This kind of stimuli allows a controlled parametric tuning of several stimulus characteristics, as well as a large number of versatile transformations. In addition to classical normalization parameters, including RCJ, we measured a dot threshold estimating the information content needed for recognition and categorization. Overall, the present results are consistent with those reported in the literature with another kind of visual stimuli, indicating this stimulus set is well suited for use in a variety of experiments, with healthy subjects or patients. In addition to the usual normalization data available with other image sets, the possibility to measure a recognition threshold, in terms of the numbers of dots, offers a quantitative evaluation of recognition performance, a feature rarely available with other stimulus sets.
Methods For the sake of the presentation, we assume that the data consist of allele frequencies in each population and that the method relies on the empirical correlation matrix between populations. In the Results section, we show that the proposed approach is also appropriate with other pairwise matrices such as F ST matrices between populations or correlation matrices between individuals. To assess local genetic differentiation around a given sampled site, we estimate the correlation of allele frequencies between the sampled population and fictive populations located in the neighborhood of the sampling sites. Neighboring populations are located at a fixed and short distance from the sampled populations, and we measure the expected local correlation (averaged over neighbors) of allele frequencies between the sampled population and the neighboring fictive populations (Fig.
Nonstationary patterns of IBD among the swedish population We first illustrate the kriging methodology using a human SNP dataset with a particularly dense geographic sampling. The data consist of genome-wide SNPs for 5174 Swedish individuals that cover all of the 21 Swedish counties  Humphreys et al. (2011). To assign each individual to a county,  Humphreys et al. (2011)  used available geographic information with the following order of priority: city or village of birth, county of birth, municipality or city of residence, and county of residence if it is the only information available. They found strong differences between far northern counties and remaining counties, and also showed that northern counties are more clearly genetically differentiated from each other than southern counties are from each other.
We consider various choices of distances for the Swedish dataset (10-150 km) and find that all these choices of distance provide similar patterns of local genetic differentiation although the patterns generated with the larger distances are smoother (Fig. S15 ). The last caveat to bear in mind concerns the choice of the pairwise dissimilarity or differentiation matrix. For instance, in a simulation study with five populations,  Lawson and Falush (2012)  showed that the five populations were clearly distinguishable with some but not all pairwise dissimilarity matrices between individuals. Although a potential caveat, being able to choose the measure of dissimilarity also adds to the flexibility of the method and different measures can convey information about processes that occurred at different time periods.
, 1986; Tiller and Link, 1993; Pfannschmidt and Link, 1994; Link, 1996; Baginsky et al. , 1997). Isolation of cotyledons in the order of kilograms is easily achieved after just 5 days of growth and provides enough material even for the biochemical analysis of low-abundant proteins by chromatography followed by mass spectrometry. Since Sinapis is a close relative of Arabidopsis, peptide data evaluation for the identification of mustard plastid proteins was found to be applicable for well conserved proteins by using the A. thaliana or Brassicales protein databases  (Schröter et al. , 2010; Steiner et al. , 2011). Thus, the use of mustard as a source for cotyledons combines the advantages of mustard chloroplast preparation with the availability of protein data of well studied organisms like A.
IDENTIFICATION OF PROTEINS FROM THE PC FRACTION BY LC-ESI-MS/MS All 1079 spots were cut out and proteins were subjected to an ingel tryptic digest. In 153 cases, selected spots were pooled from duplicate gels in order to increase the protein amount for the subsequent measurements. Since a database from S. alba is currently not available, protein identification was performed by comparing the determined mass spectrometry data to the Brassicales and A. thaliana databases  (compare Materials and Methods). By this means 225 proteins were reliably identified with at least two different peptides in 180 spots indicating that several spots contained more than one protein. In addition, 36 particular proteins were identified in more than one spot (up to 40 different ones) suggesting post-translational modification of these proteins (Table  1 ).
A practical classification mode is given by the modified MapMan bin system  (Thimm et al. , 2004)  of the Plant Proteomics Data Base (PPDB)  (Sun et al. , 2009). In Table  1 , proteins were listed following the PPDB bin grouping as given in column 2. For further comparison, we summarized identified proteins into five major groups. The first group comprises transcription and transcript related proteins, namely subunits of the plastid encoded RNA polymerase (PEPs) and PEP associated proteins (PAPs) as defined in  Steiner et al. (2011) , other pTACs (pTAC proteins not belonging to the PAPs) and RNA and DNA related proteins (bin 27 and 28, not belonging to PAPs and pTACs). A second large group comprises translation related proteins (bin 29. 2 and 29. 5). Three further groups cover proteins involved in protein homeostasis (bin 29 and 21 not belonging to PEPs and PAPs), photosynthesis (bin 1) and a miscellaneous group called "others" including various enzymes catalyzing metabolic reactions or protein modifications.
TRYPTIC DIGEST, LC/ESI-MS/MS AND DATA ANALYSIS The spot pattern of the different gels was compared. Matching low abundant spots were pooled (as indicated in Supplemental Table  1 ) to increase the detectable protein amount. Tryptic digest of protein spots was conducted after destaining as referred  (Mørtz et al. , 1994; Stauber et al. , 2003). Mass spectrometry was carried out at LCQ™-DecaXP ion trap mass spectrometer (Thermo Finnigan, San Jose, CA, USA) using a data-dependent scan procedure with four cyclic scan events as described in  Schröter et al. (2010). The first cycle, a full MS scan of the mass range m/z 450-1200, was followed by three dependent MS/MS scans of the three most abundant ions. Sample run and data acquisition was performed using the Xcalibur™ software (Version1. 3 © Thermo Finnigan 1998-2001).
Seventy-six of the low abundant spots were measured at a Finnigan LTQ linear ion trap mass spectrometer (Thermo Finnigan, Thermo Fisher Scientific Inc. , Waltham, MA, USA) coupled online after a nano HPLC Ultimate 3000 (Dionex, Thermo Fisher Scientific Inc. , Waltham, MA, USA)  (Schmidt et al. , 2006). After one full MS the instrument was set to measure the collision induced dissociation pattern of the four most abundant ions and exclude the measured once for 10 s from newly measuring. The resulting spectra were analyzed using the Proteome Discoverer vs. 1. 0 (Thermo Fisher Scientific Inc. , Waltham, MA, USA) with the implemented Sequest algorithm  (Link et al. , 1999). Therefore, a database of all RefSeq (reference sequence) sequences of A. thaliana and Arabidopsis lyrata as well as the complete Brassica napus and Capsella rubella and the remaining Brassicales proteins of NCBI was created  [NCBI 2012.
03. 19   (Veith et al. , 2009). All proteins with at least two unique peptides were taken for further analysis. For transit peptide prediction the web-based tools TargetP (http://www. cbs. dtu. dk/services/TargetP/) (Emanuelsson et al. , 2000)  was used and for prediction of the transit peptide length the web-tool ChloroP (http://www. cbs. dtu. dk/services/ ChloroP/)  (Emanuelsson et al. , 1999). For further analyses identified proteins were grouped into bins according to the modified MapMan system of the plant proteome database (ppdb) (http://ppdb. tc. cornell. edu/dbsearch/mapman. aspx)  (Sun et al. , 2009)  based on the MapManBins of  Thimm et al. (2004)  (Supplemental Table  2 ).
955 RC 1 This work (PRPL12-3) (Continued) Thimm et al. (2004) are named in the first column according to the annotation of the respective gene at NCBI. They are grouped into different classes written bold at the beginning of each group as defined in results. Proteins within each group are sorted alphabetically. Spots: number of the spots containing the respective protein. MapMan bin: classification groups for proteins according to the modified MapMan system of the plant proteome database (ppdb) [(http:// ppdb. tc. cornell. edu/dbsearch/mapman. aspx)©Klaas J. van Wijk Lab, Cornell University;Sun et al. , 2009]based on the MapManBins ofThimm et al. (2004); Accession: gi identification number and At gene accession number; cTP: possibility of a plastid transit peptide and the respective reliability class (RC); References: first identification of the protein in mustard by mass spectrometry.
Phylogeny Sequences ranged between 609 and 624 bp (DNA polymerase gene or dpo) for viruses and 1996 and 2238 bp (rDNA 18S (1620-1635 bp) + ITS2 (366-607 bp)) for hosts. Respective alignment lengths were 624 bp (208 AA) and 2289 bp. Unpublished sequences were deposited in GenBank (Accession numbers, sequence lengths, strain names, geographical origin, and host culture for isolation are given in Tables  2  and 3 ). Evolutionary models selected with jModelTest were Tamura-Nei 93 + I + G (accounting for rate heterogeneity across sites via a Gamma distribution with a 0. 327 alpha parameter and including 41% of invariant sites) for hosts (18S + ITS), and CpREV + G + F (using observed AA frequencies, and a 0. 78 alpha parameter) for the alignment of virus protein sequences. Whatever the methods (Bayesian inference (BI) or maximum likelihood (ML)) and datasets (DNA or AA for viruses), phylogenetic trees were the same for hosts and very similar for viruses, therefore only BI trees are presented (Figure  1.
Cophylogeny The global congruence between trees using the distancebased approach ParaFit was highly significant (P = 0. 001, see Table  4  for all results on cophylogenetic analyses), as were all the 138 individual host-virus links (each P-value is below 0. 05). The event-based analysis with Jane, taking tree topologies into account, also yielded a highly significant global congruence between host and virus phylogenetic trees (P = 0. 001), confirming the ParaFit analysis. The    complexity of this host-symbiont system precludes the establishment of any intelligible coevolutionary scenario. OtV573 - - - - - - - - - - - - - □ - - - - ■ - - - OtV22 - - - - - - - - - - - - - - - - - □ - - - - OtV343 - - - - - - - - - - - - - - - - □ - - - - - OtV344 - - - - - - - - - - - - - - - - □ - - - - - OtV304 - - - - - - - - - - - - - - - □ - - - - - - OmV63 - - - - - - - - - - - - - - - - - - - □ - - OmV64 - - - - - - - - - - - - - - - - - - - □ - - OmV67 - - - - - - - - - - - - - - - - - - - □ - - BpV1 - - - - - - - - - - - - - - - - - - - - □ - BatV3 - - - - - - - - - - - - - - - - - - - - - □ Ol_CCE9901 Because of this complexity, we performed three partial analyses in addition to the full dataset: only Micromonas strains and their viruses, Bathycoccus and Ostreococcus strains and their viruses, and only Ostreococcus strains and their viruses.
Analyses with ParaFit gave a significant global congruence (P = 0. 001) between host and parasite trees for the Micromonas dataset (78/111 significant links) and the Bathycoccus and Ostreococcus dataset (12/26 significant links) but some individual host-virus links were found non-significant. For the Ostreococcus dataset, the global fit was not significant (P = 0. 11; 1/25 significant links). In Jane analyses, the global congruence was significant for the Micromonas dataset (P = 0. 001), but not for the Bathycoccus-Ostreococcus dataset (P = 0. 1) and the Ostreococcus dataset (P = 0. 46). Note that the results were similar with the trees obtained from BI or ML trees, and using a virus tree where the monophyly of the viruses of Micromonas, Ostreococcus and Bathycoccus were each enforced (built with the same method and parameters, and not significantly different from the tree on Figure  1  (Kishino-Hasegawa test: P = 0.
This is especially relevant, given that low density populations of host Mamiellales are often found in oligotrophic environments  [34, 35]. "All" refers to the full dataset, "Micro" to the association between only Micromonas hosts and their viruses, "Bathy-Ostreo" to Bathycoccus and Ostreococcus hosts and their viruses. All statistical tests were performed with 999 permutations. Some host strains (e. g. Micromonas RCC2484 or Ostreococcus tauri RCC745) are susceptible to a wider range of viruses than others (e. g. Micromonas RCC804 or Ostreococcus RCC1108). This may be related to lower resistance of these Mamiellale strains to viruses, as previously observed by Thomas et al. [30] , who showed experimentally that resistance to a given viral strain was associated to increased sensitivity to other viral strains as well as to loss in fitness (i.
In a vast aquatic environment, such as the open sea, the barriers to host switching can be seen as generally weaker than in a structured terrestrial ecosystem where local adaptation can occur more easily  [51] , especially given the wide dispersal of hosts and viruses in the marine ecosystem. Any cospeciation signal is thus more likely due to close adaptation to the host than to the impossibility to switch hosts. A significant global signal of cospeciation was found with all methods used, suggesting that Prasinovirus evolution is in part driven by the evolution of their hosts, or at least that related viruses tend to use related hosts. When partitioning the dataset into Micromonas strains and their viruses, Bathycoccus-Ostreococcus strains and their viruses, and Ostreococcus strains and their viruses, results were slightly different.
The global fit analysis with ParaFit found a significant congruence in all cases, while the event-based analysis with Jane found a non-significant congruence for the Bathycoccus-Ostreococcus and the Ostreococcus datasets. This lack of significant signal may reflect a genuine lack of cophylogenetic signal, or may be due to a lower statistical power with less data (because the null hypothesis is the absence of congruence). The differing results obtained by ParaFit and Jane for Bathycoccus-Ostreococcus and Ostreococcus only might also arise because the genetic distances (used by ParaFit) do not always correlate with phylogenetic (patristic) distances as used in Jane. Thus genetically close viruses tend to colonize close hosts but this may not be always the case at a phylogenetic point of view supporting the hypothesis that viruses can switch to different, but not too distantly related, host strains.