dataesr/scientific-paragraphs-categorization huggingface dataset:
4  does not allow one to examine the ancient samples in relation to contemporary East Asian populations, we also carried out PCA of all 2,345 modern individuals in the Human Origins dataset  35  , onto which we also projected the ancient individuals (Fig. 5 ). It is evident from this PCA that ancestry of the Iron Age samples falls on a continuum between present-day west Eurasians and eastern non-Africans, which is in concordance with the mitochondrial haplogroup analyses. The eastern Scythians display nearly equal proportions of mtDNA lineages common in east and west Eurasia, whereas in the western Scythian groups, the frequency of lineages now common in east Eurasia is generally lower, even reaching zero in four samples of the initial Scythian phase of the eight to sixth century BCE (group #1 in Fig.
Phenotypic markers. Derived alleles of pigmentation markers that are under selection in Europeans are present in eastern and western Scythians, including individuals who are homozygous for the derived alleles at selected SNPs in the HERC2, SLC24A5 or SLC45A2 (ref. 43-45). At the two LCT loci associated with lactase persistence, the derived allele is observed only in heterozygotes, only in the eastern Scythian samples, and at low frequency (2-3%). The ancestral alleles at ADH1B rs3811801 and rs1229984 are nearly fixed in the Scythian dataset, as they are in modern Europeans (the derived alleles, which confer some resistance to alcoholism, are under selection in East Asians  46, 47  ). We observe the derived allele at rs3827760 in the EDAR gene in a single Pazyryk individual (#6 in Fig. 2 ).
Sample preparation, DNA extraction, the amplification of single mtDNA fragments and the amplification of nuclear loci were performed as previously described  45. For mtDNA analyses 30 coding region fragments covering 32 haplogroup-specific SNPs of the mitochondrial genome were selected from the literature  [51] [52] [53]. Primer systems (Supplementary Table  3 ) were designed using PrimerSelect, a part of the Lasergene software package (DNASTAR). Amplification was performed in three different multiplex reactions to avoid overlapping fragments (Supplementary Table  3 ). The Multiplex PCRs were carried out in a total volume of 40 ml, using 20 ml Qiagen Multiplex Kit Master Mix (Qiagen, Hilden, Germany), 11 ml ddH 2 O, 0. 2 ml of each primer and 4 ml DNA extract. Multiplex PCR conditions were as follows: initial denaturation for 15 min at 95 °C followed by 36 cycles of 40 s denaturation at 94 °C, 40-90 s annealing at 56 °C, 40-90 s elongation at 72 °C and for some reactions a final elongation for 10 min at 72 °C was added.
The Arlequin 3. 5. 1. 3 software 59 was used for AMOVA and to calculate gene diversity (haplotype diversity)  60  , nucleotide diversity  60, 61  , F ST values and Reynolds' distance  62. The pairwise distance method was used to calculate the F ST values with 1,000 permutations and a gamma value ¼ 0. Fu's F S test of selective neutrality was performed to test for population expansion. The significance level for the F S values was set at 0. 02 (ref. 63). For results of summary statistics see Supplementary Table  7. ABC analyses. To explore the demographic history of Scythians we formulated multiple candidate scenarios, which provided the basis for simulating samples of genetic data for the HVR-1 region, using BayeSSC  64, 65. Calculations of summary statistics for the observed data were performed in DNaSP v5 (ref.
66). To confirm that candidate scenarios were able to reproduce the observed genetic data, we compared the prior distributions of simulated summary statistics with the empirically observed values. All analyses were performed using the abc package in R 2. 15. 1 (refs 67,68). We refer to the Supplementary Note 1 for full details on demographic scenarios and analyses employed here. Genomic analyses. DNA-library preparation for subsequent shotgun sequencing was performed according to the protocol used in Kircher 2012 (ref. 69) with slight modifications for the shotgun samples (Supplementary Note 3). To analyse the genomic data f 3 -and f 4 -statistics were calculated (qp3Pop and qpDstat from ADMIXTOOLS)  37  , analysis of ancestry streams was applied (qpWave/qpAdm)  26  and an ADMIXTURE analysis was performed  38, 39  (Supplementary Note 2).
Acknowledgements We would like to thank  Leonid Yablonsky  for providing the Pokrovka samples and  Jeannine Davis-Kimball  for her help during the sampling process;  Maria Mednikova  and  Maria Dobrovolskaya  for the provision of the samples from Novozavedennoe and Kolbino, respectively;  Anton Gass  and  Anatoli Nagler  for additional archaeological information. Some computationally intensive analyses were run on the  Linux cluster of the 'Museum National d'Histoire Naturelle' in Paris  (administrated by  Julio Pedraza Acosta ). D. R. was supported by  NIH  grant  GM100233 , by  NSF   HOMINID BCS-1032255 , and is a  Howard Hughes Medical Institute investigator. This work was sponsored by the  German Federal Ministry of Education and Research. M. C. was supported by  Swiss NSF  grant  31003A_156853.
(Duh and Kirchhoff, 2005). In this paper, we present the Tunisian Arabic Morphological DisAmbiguation System (TAMDAS). This system uses the output of a Tunisian Dialect (TD) morphological analyzer (Al-Khalil-TUN)  (Zribi et al. , 2013b)  and a TD corpus (the STAC corpus)  (Zribi et al. , 2015) , to morphologically disambiguate TD annotated transcriptions. TAMDAS tests three different classifiers and combines their results with a bigram module in failure cases. We build a classifier based on feature vectors, which are generated from the morphologically annotated corpus, and then use it to classify the possible analyses of each word into correct and false classes. This paper has seven main sections. Section 2 presents an overview of previous works that studied TD and the POS tagging of dialectal Arabic.
However, the automatic processing of Tunisian Dialect (TD) and its spoken varieties has not received much attention. The DA POS tagging techniques follow two principal approaches. The first approach suggests using MSA resources and a few DA resources to create a POS tagger. In this context,  (Duh and Kirchhoff, 2005)  used the Buckwalter Morphological Analyzer  (Buckwalter, 2004)  designed for MSA, the LDC MSA Treebank corpus and some dialectal resources (the CallHome Egyptian Colloquial Arabic corpus, the LDC Levantine Arabic corpus) in combination with unsupervised learning algorithms in order to develop a POS tagger for Egyptian Arabic. The authors proposed to bootstrap the HMM tagger using POS information from the morphological analyzer. They improved the tagger by integrating additional data from other dialects  (Duh and Kirchhoff, 2005).
They reported a POS accuracy of 70. 9%. Likewise,  Rambow et al. (2006)  explored MSA data and resources to develop a POS tagger for Levantine dialect. They adapted an MSA POS-tagger for Levantine data. They suggested that leveraging the existing resources is a viable option. Rambow et al. (2006)  developed a bilingual small lexicon MSA/Levantine dialect. Combining information from this lexicon and a parameter renormalization strategy based on minimal linguistic knowledge,  Rambow et al. (2006)  noticed the biggest improvement in the tagger. Moreover,  Habash et al. (2013)  developed a morphological analysis and disambiguation for Egyptian Arabic based on an existing tool for MSA (the MADA tool,  Habash and Rambow (2005)  and  Roth et al. (2008). MADA uses an existing morphological analyzer of MSA and applies a set of models (support vector machines and N-gram language models) to produce a per word in-context prediction.
Tunisian dialect morphological analyzer (Al-Khalil-TUN) Only a few tools have been developed to analyze the morphology of DA. Generally, proposed methods for DA morphology focus on extending MSA tools to cover DA phenomena  (Habash et al. , 2012b; Habash and Rambow, 2006; Salloum and Habash, 2014). In comparison, only two works  (Hamdi, 2015; Zribi et al. , 2013b)  focus on TD. In the present work, we utilized Al-Khalil-TUN  (Zribi et al. , 2013b)  released under a license that made it free software, unlike the analyzer proposed by  Hamdi (2015). Al-Khalil-TUN  (Zribi et al. , 2013b)  is an adapted version of the MSA morphological analyzer Al-Khalil  (Boudlal et al. , 2010)  which is a ''root-pattern" based morphological analyzer. To adapt this analyzer,  (Zribi et al. , 2013b ) created a TD lexicon composed of roots and patterns related to their morphological characteristics  (Zribi et al.
Method overview The aim of this work is to build a MD system for TD. Our starting point is the output of the morphological TD analyzer Al-Khalil-TUN  (Zribi et al. , 2013b). We propose to extend the analyzer by developing and integrating a disambiguation module. Fig. 1  presents the architecture of the TAMDAS system. The main steps of our system for tagging TD are as follows: -Automatic sentence boundary detection of transcribed TD. We aimed at the integration of an automatic model for identifying sentence boundaries. We integrated the system developed by  Zribi et al. (2016) , which can detect boundaries of transcribed oral sentences. This system uses three different methods to detect the sentence boundary (rule-based, statistical and hybrid methods). We have applied the statistical method that gives the best evaluation results.
The training dataset is randomly divided into two subsets: a growing set, which usually consists of 2/3 of the examples, and a pruning set, which consists of the remaining 1/3. The growing set is used for the initial rule construction (the rule growth phase) and the pruning set is used for pruning (the rule pruning phase). A minimum Description Length (MDL) based heuristic is used as a criterion to stop the rule construction process  (Cohen, 1995; Mohamed et al. , 2013). 6. 1. 1. 2. PART. It is a partial decision tree algorithm, which is the developed version of C4. 5 and RIPPER algorithms  (Mohamed et al. , 2013). The most important feature of the PART algorithm is that it does not need to perform a global optimization like C4. 5 and RIPPER to generate accurate rules, but it follows a ''separateand-conquer" strategy.
Evaluation and discussion In this paper, we presented our method to create TAMDAS, a system for POS tagging TD in a spoken variety of Arabic language. To train and test TAMDAS, we divided the STAC corpus into two sets. We used 35708 words for the training of our systems (TAMDAS and the baseline) and 6680 words for the evaluation. To test the performance of our system, we used two tag sets. The first one does not contain POS tags for oral phenomena. The second one incorporates the following tags: onom, TrunW, interj, FPause and break to respectively mark onomatopoeia, truncated words, interjections, filled pauses, and silent pauses. Tables  5  and 6  present the error rates of words correctly classified with and without speech tags based on three classifiers. We ignored the step of bigram classifier when calculating these values.
(2014) )'s system. The work of  Jarrar et al. (2014) ) on the Palestinian dialect showed that use of MADAMIRA makes a good initial baseline (78%). This can be justified by two reasons. First, the Palestinian dialect is very close to the Egyptian one as they have multiple characteristics in common. These dialects belong to Eastern dialects. In contrast, TD belongs to another group of dialects (Western dialect) that present multiple differences. Second, the training corpus of our system is different from MADAMIRA system's. Our training corpus is based on oral transcripts but the MADAMIRA training corpus is related to the written form of Egyptian and MSA texts. The failure cases of our system are generally due to semantic ambiguity. Let us take the TD sentence: ‫ﺭ‬.
9/14/21, 12:13 PM Saving Our Marine Archives -Eos https://eos. org/science-updates/saving-our-marine-archives 3/7 Within these communities, the need for standards and metadata is particularly important for Within these communities, the need for standards and metadata is particularly important for scientists who use marine specimens with relatively high accretion rates-corals, mollusks, coralline scientists who use marine specimens with relatively high accretion rates-corals, mollusks, coralline algae, sclerosponges (hard sponges), varved sediments, and the like-as samples, metadata, and data algae, sclerosponges (hard sponges), varved sediments, and the like-as samples, metadata, and data are scattered in individual labs and select repositories. are scattered in individual labs and select repositories.
However, these metadata are not necessarily linked to the physical samples reconstruction variables. However, these metadata are not necessarily linked to the physical samples and other important sample metadata such as sample size, field site pictures, and imaging of the and other important sample metadata such as sample size, field site pictures, and imaging of the samples (e. g. , X-rays, thin sections, etc). samples (e. g. , X-rays, thin sections, etc). Paleoclimate research has produced a critical mass of data that is now comprehensive enough to be Paleoclimate research has produced a critical mass of data that is now comprehensive enough to be used for large-scale research syntheses. used for large-scale research syntheses. From the ever-increasing array of marine annually resolved proxy archives, the paleoclimate research From the ever-increasing array of marine annually resolved proxy archives, the paleoclimate research endeavor has produced a critical mass of data that is now comprehensive enough to be used for large-endeavor has produced a critical mass of data that is now comprehensive enough to be used for largescale research syntheses.
Registering Metadata on Physical Samples Registering Metadata on Physical Samples Our first recommendation is to use the System for Earth Sample Registration ( Our first recommendation is to use the System for Earth Sample Registration (SESAR SESAR (http://www. geosamples. org/) (http://www. geosamples. org/)). SESAR, a registry for physical samples taken from the natural ). SESAR, a registry for physical samples taken from the natural environment, has the capacity needed to store physical sample metadata for the annually resolved environment, has the capacity needed to store physical sample metadata for the annually resolved proxy community. SESAR catalogs and archives sample metadata, and it offers tools and services for proxy community. SESAR catalogs and archives sample metadata, and it offers tools and services for users to manage their sample metadata and to obtain an International Geo Sample Number ( users to manage their sample metadata and to obtain an International Geo Sample Number (IGSN IGSN (http://www.
for referencing physical samples. Registering samples in SESAR is easy: investigators can download a template as an Excel file, save it Registering samples in SESAR is easy: investigators can download a template as an Excel file, save it on a personal computer, and upload data to SESAR using this template at their convenience. on a personal computer, and upload data to SESAR using this template at their convenience. Investigators are able to control the privacy of their sample metadata, meaning that they can store Investigators are able to control the privacy of their sample metadata, meaning that they can store and manage their physical sample metadata in SESAR, obtain IGSNs for each sample, and make that and manage their physical sample metadata in SESAR, obtain IGSNs for each sample, and make that information public in SESAR only when they are ready: upon publication release, for example.
information public in SESAR only when they are ready: upon publication release, for example. Physical sample IGSNs can be inserted into publications, saving space and time by providing a Physical sample IGSNs can be inserted into publications, saving space and time by providing a compact reference to all the information on the physical samples that is freely accessible online, compact reference to all the information on the physical samples that is freely accessible online, rather than cluttering journal articles with metadata that are important but perhaps peripheral to the rather than cluttering journal articles with metadata that are important but perhaps peripheral to the particular scientific findings of the article. The IGSN can link to the catalog of an institutional particular scientific findings of the article.
The IGSN can link to the catalog of an institutional repository, where physical samples are stored and curated. repository, where physical samples are stored and curated. SESAR facilitates metadata management and may make sample management easier, for example, by SESAR facilitates metadata management and may make sample management easier, for example, by providing the ability to easily print labels. The Lamont Doherty Earth Observatory Core Repository providing the ability to easily print labels. The Lamont Doherty Earth Observatory Core Repository has taken advantage of these capabilities to organize and catalog their physical samples (Figure  1 ). has taken advantage of these capabilities to organize and catalog their physical samples (Figure  1 ).
retrieval of their geochemical data. In addition to promoting the use of SESAR to store physical sample metadata and training In addition to promoting the use of SESAR to store physical sample metadata and training investigators to use SESAR, the MARPA project has defined several goals for the near future. We are investigators to use SESAR, the MARPA project has defined several goals for the near future. We are asking the MARPA community to describe their needs and desires regarding the storage and retrieval asking the MARPA community to describe their needs and desires regarding the storage and retrieval of their geochemical data. We have and will continue to incorporate our community's input into of their geochemical data. We have and will continue to incorporate our community's input into communities move forward with their specific archival issues.
We will use the MARPA website forum communities move forward with their specific archival issues. We will use the MARPA website forum section to discuss various ongoing aspects of our community needs, including how to store physical section to discuss various ongoing aspects of our community needs, including how to store physical samples that have already been analyzed once their metadata have been registered in SESAR. samples that have already been analyzed once their metadata have been registered in SESAR. More information on the MARPA project, updates on recent activities, and channels for providing More information on the MARPA project, updates on recent activities, and channels for providing feedback are available on the feedback are available on the MARPA website MARPA website (https://marpasite.
including those at the National Oceanic and Atmospheric Administration's National Centers for including those at the National Oceanic and Atmospheric Administration's National Centers for Environmental Information Paleoclimate Program ( Environmental Information Paleoclimate Program (NOAA-Paleo NOAA-Paleo (https://www. ncdc. noaa. gov/data- (https://www. ncdc. noaa. gov/dataaccess/paleoclimatology-data) access/paleoclimatology-data)) and ) and PANGAEA PANGAEA (http://www. pangaea. de/) ( http://www. pangaea. de/), which is hosted by the German , which is hosted by the German organizations Alfred Wegener Institute Helmholtz Center for Polar and Marine Research (AWI) and organizations Alfred Wegener Institute Helmholtz Center for Polar and Marine Research (AWI) and Center for Marine Environmental Sciences (MARUM) at the University of Bremen.
Acknowledgments Acknowledgments We thank the current and former  NOAA  Paleoclimate team for helpful discussions,  David Anderson , We thank the current and former  NOAA  Paleoclimate team for helpful discussions,  David Anderson ,  Eugene Wahl ,  Carrie Morill , and  Bridget Thrasher , as well as  Kerstin Lehnert  and  Megan Carter  of  Eugene Wahl ,  Carrie Morill , and  Bridget Thrasher , as well as  Kerstin Lehnert  and  Megan Carter  of the Interdisciplinary Earth Data Alliance, the data facility that operates SESAR. We also thank the the  Interdisciplinary Earth Data Alliance , the data facility that operates SESAR. We also thank the attendees of the first MARPA workshop during the 2014 Ocean Sciences Meeting and the informal attendees of the first MARPA workshop during the 2014 Ocean Sciences Meeting and the informal MARPA town hall meeting at the 2015 AGU Fall Meeting.
Data collection The participant was asked to play legato at 2 tempi (60 and 90) on the 4 different strings (G, D, A, E) of his own violin using his personal bow. Then, he was instructed to play spiccato on the A string at a tempo of 120 with three bow configurations (a control configuration and two configurations where a 5 g mass was added on the frog or the tip of the bow). Three repetitions were performed for each of these three configurations. Hence, 8 legato trials and 9 spiccato trials were performed for a total of 17 trials. EMG data were recorded during 5 cycles of bow down and up. The skin markers trajectories were recorded using an optoelectronic system. These trajectories allow the identification of specific moments in the bowing motion cycles.
Data treatment The raw EMG signals were filtered using a zero-lag 4 th -order Butterworth band-pass filter between 20 and 400 Hz. Then, the Root Mean Squared (RMS) EMG were computed using a 50 ms sliding window  (Blache et al, 2016). For each trial and each muscle, the mean muscular activity was calculated by averaging the RMS EMG and normalizing this value using the average over the 17 trials. Finally, the standard deviations, averages and variation coefficients (i. e. , standard deviation divided by the average) of the mean muscular activity were calculated over the three repeated trials. The influence of the string played, the tempo and the added mass was assessed by comparison to the mean muscular activities.
Finally, without local inquiry, it would be impossible to assess the urban character of dense invisible settlements, and whose urban character is currently cloaked in ambiguity. In order to understand the role of these settlements within the urban system, field studies also need to go beyond the local story and recognize the rural-urban and urban-urban linkages and the relationships with various levels of government. This would reconnect with work on spatial and regional datasets that characterise their location and socio-economic context. Consequently, a sincere approach to understanding subaltern urbanisation requires a combination of field studies and analysis of spatial data and socio-economic data available through the census and national sample surveys, national and state accounts, etc.
A shortcoming of many self-reported antipsychotic AE questionnaires is that differences among these tools (e. g. , individual items included, overall scope) impede direct comparison of their results. For example, in a comparison of four self-rating scales completed by 320 patients with schizophrenia, results did not strongly correlate across questionnaires, at least in part because of the items measured  [16]. The objective of the current analysis was to develop a tolerability index for schizophrenia treatments that combines the frequency, severity, duration, and patient-perceived importance attributed to AEs into a single score; includes the patient perspective; can be computed on any dataset that includes patient-level AE data; and provides a scoring system that allows characterization and direct comparison of the overall tolerability of medications used for treating patients with schizophrenia.