dataesr/scientific-paragraphs-categorization huggingface dataset:
RobeRt Hettel APS-U Project Director, ANL, Argonne, Illinois, USA Wayforlight: The Catalogue of European Light Sources W ayforlight. eu [1] is the gateway to find- ing the most suitable instruments for experiments with synchrotron, FEL, and laser light sources. The portal's main asset is a detailed searchable catalogue of facilities, beamlines, and instrumentation available at European light sources. Thanks to its advanced search tools, a visitor can filter beamlines by scientific discipline, by technique, but also by energy range or sample type. The original dataset included European synchrotron and free electron laser (FEL) sources, and was created under the EC FP7-funded project CALIPSO. The Horizon 2020 EUCALL project  [2]  grouped together advanced light sources, including synchrotrons, FELs, and op-tical laser light sources.
The experts involved in EUCALL performed a comprehensive analysis of the technical and scientific resources on offer at such sources, and proposed recommendations for optimal use of these facilities. For the benefit of the different and growing user communities, the information was incorporated into the wayforlight database, integrating the laserdriven advanced light sources for the first time in the same catalogue. The newly augmented database, created and maintained at Elettra, became publicly available in September 2018. The facilities and beamline datasheets are standardized; users are now able to explore the database and directly compare instrumentation at a much larger number of facilities in greater depth, allowing them to develop more elaborate, comparative, and complementary experiments at various facilities.
The capabilities of the wayforlight database were also extended by including a set of Advanced Programming Interfaces (APIs), such that information entered by instrument scientists can be automatically displayed on their own individual facility website. In this way, description of instrumentation at the participating facilities has been standardized, while avoiding the disadvantage that beamline Plan for one of the 7-bend achromat sectors. scientists have to manually enter instrumentation data into multiple platforms. These two factors represent the key towards a sustainable future for the catalogue as a whole. Discussions on further data integration are presently ongoing with several facilities, as well as with the FELs of Europe association and the global website lightsources. org [4]. Wayforlight was also presented at a MERIL  [5]  meeting, and the possibility to interlink the two portals has been considered to be very promising.
Acknowledgments The author thanks  Michael Borland ,  Steve Davey ,  Glenn Decker ,  Dean Haeffner , and  Mohan Ramanathan  for their assistance in preparing this article. ■ Acknowledgments CB warmly thanks her colleagues  C. Callegari ,  A. Lausi ,  A. Locatelli ,  T. O. Mentes ,  M. Zangrando ,  D. Garzella , and  B. Schramm  for fruitful discussions on the portal policy and database architecture.
Background and aims: Binge-watching (i. e. , watching multiple episodes of a TV series in one session) has recently become standard practice among TV series viewers; this expansion generates concerns regarding the potential negative outcomes associated with this habit. However, the investigation of its psychological correlates remains fragmentary, with few initial studies a priori conceptualizing this behavior as a new addictive disorder. This study explored these psychological correlates using cluster analysis of binge-watching behavior based on three key psychological factors: motivations, impulsivity, and emotional reactivity. Methods: An online survey was completed by 4,039 TV series viewers. Data were analyzed using hierarchical and non-hierarchical cluster analyses, the validity of the clusters being finally determined through mutual comparisons with a selection of external correlates.
In fact, high emotional reactivity is a hallmark of psychopathology, and it is worth stressing that previous studies found positive associations between high emotional reactivity, reduced executive/ inhibitory control, and problematic behaviors (e. g. ,  Nock, Wedig, Holmberg, & Hooley, 2008; Pessoa, 2009; Silk, Steinberg, & Morris, 2003). The aim of this study was, therefore, to explore bingewatchers' psychological characteristics by identifying potential subgroups of TV series viewers, taking into account the combination of motivations for TV series watching, impulsivity traits, and emotional reactivity, and, ultimately, to clarify whether certain subgroups of viewers are more susceptible to problematic binge-watching. To this end, we used cluster analysis on data from a large sample of TV series viewers, where affect (as an indirect measure of comorbid psychopathology) and co-occurring problematic (non-substance-and substance-related) behaviors were part of the external correlates being investigated.
Statistical analyses All statistical analyses were performed with IBM SPSS Statistics 24  (IBM Corp, 2015). Subgroups of binge-watchers were determined as usually advised by means of data clustering executed via both hierarchical and non-hierarchical techniques  (Hair, Black, Babin, & Anderson, 2010). In the first step, a hierarchical analysis was conducted by applying Ward's clustering method based on squared Euclidean distances; then, cluster membership was identified in a second step with non-hierarchical K-means analysis. In accordance with the above theoretical rationale, the set of psychological variables of interest (i. e. , TV series-watching motivations, impulsivity traits, and emotional reactivity) was used to form the clusters after prior Z-score transformation. In addition, upstream inspection of the correlations between each of these variables (the full correlational results are available from https://osf.
RESULTS The data clustering results derived from the agglomeration schedule, dendrogram, and scree plot  (Yim & Ramdeen, 2015)  suggested an optimal four-factor solution at both statistical and theoretical levels. Additional analyses revealed that a three-factor solution would mask insightful findings by merging clusters 2 and 4, whereas a five-factor solution includes two very similar clusters. The psychological profiles of the four clusters identified are displayed in Figure  1. The subsequent ANOVAs emphasized that cluster membership has a significant effect on all psychological factors considered (i. e. , TV series-watching motivations, impulsivity dimensions, and emotional reactivity), and on all the external correlates (i. e. , sociodemographics, viewing patterns, self-perceived consumption and preferences related to TV series, reported involvement in binge-watching and problematic binge-watching, affect, problematic Internet use, and alcohol-related problems).
Purpose: News media plays an important role in formulating people's knowledge and opinions about various aspects of life, including health. The current study explored how hearing loss and hearing aids are represented in the U. S. newspaper media. Method: A cross-sectional study design was selected to analyze publicly available newspaper media data. The data sets were generated from the database, the U. S. Major Dailies by ProQuest, by searching key words for newspapers published during 1990-2017. Cluster analysis (i. e. , text pattern analysis) and chi-square tests were performed using Iramuteq software. Results: The hearing loss data set had 1,527 texts (i. e. , articles). The cluster analysis resulted in 7 clusters, which were named as (1) causes and consequences (26. 1%), (2) early identification and diagnosis (9%), (3) health promotion and prevention (22.
data set had 2,667 texts. The cluster analysis resulted in 8 clusters, which were named as (1) signal processing (20. 2%), (  2 ) insurance (8. 9%), (3) prevalence (12. 4%), (4) research and development (5. 4%), (5) activities and relation (16. 2%), (  6 ) features to address background noise (13. 8%), (  7 ) innovation (12%), and (8) wireless and connectivity (11. 1%). Time series analysis of clusters in both "hearing loss" and "hearing aids" data sets indicated changes in the pattern of information presented in the newspaper media during 1990-2016 (e. g. , Cluster 7 focuses on cognitive hearing science in a hearing loss data set emerging only since the year 2012 and growing rapidly). Conclusions: The text pattern analysis showed that the U. S. newspaper media focuses on a range of issues when considering hearing loss and hearing aids and that patterns or trends change over time.
Data Extraction To develop the text corpuses, we first explored databases with the newspaper collection available at Lamar University. The U. S. Major Dailies by ProQuest was selected as the database, as it had the largest and most important newspaper collection with the top five newspapers in full-text format with significant back files. U. S. Major Dailies provides access to the five most widely circulated U. S. national and regional newspapers: The New York Times, The Washington Post, Los Angeles Times, Chicago Tribune, and the Wall Street Journal. The titles offer researchers thorough and timely coverage of local, regional, and global events with journalistic balance and perspective. The database provides archives from as far back as 1980, and all titles are cross-searchable on the ProQuest platform.
Also, the database search will result in an output of all available newspaper articles indexed in the database. Hence, we believe that this database provides indexing of newspapers with an adequate representation of U. S. newspapers. The database included indexing of newspapers in the electronic form beginning in 1990. Hence, the database was searched from 1990 to 2017. The search terms were hearing loss and hearing aids, and results were downloaded as two different corpuses. Initially, we considered searching multiple key words (e. g. , hearing loss, hearing impairment, hearing disability, deafness) about each topic to ensure we covered all the articles related to this topic while building the data corpus. However, we had to search each key word separately in the database and then search multiple text corpuses to build one corpus.
Data Analysis Data analysis included a text pattern analysis using cluster analysis. The text corpus consisted of multiple newspaper articles. The text corpuses were analyzed using the Iramuteq software  (Ratinaud, 2014) , which is an opensource software used to analyze text data. The software treats each of these articles as text (i. e. , it's the first unit). Initially, each article (i. e. , text) was segmented into smaller units referred to as text segments (i. e. , each text is split into multiple text segments based on criteria of size and punctuation). The split of the text into segments decreases the granularity of the units and thus makes it possible to increase the precision of certain analysis, in particular the search of themes within the text corpus. Following this, the text corpus was lemmatized.
This process involves sorting words into groups or variant forms of the same word, known as lemmas (i. e. , group of words in a single form). Distinction is made between "full words" (e. g. , verbs, noun, adjectives, adverbs) and "tool words" (e. g. , pronoun, determent, and useful verbs such as to be and to have). This distinction is made so that only full words are included in the main analysis. These steps are necessary in order to convert the whole corpus to easily manageable data sets for further analysis. A more detailed description about this process has been presented in our recent publication  (Manchaiah et al. , 2018). In order to specifically analyze the text that is closely related to hearing loss and hearing aids, the text segments related to these two terms were extracted, and new corpuses were formed.
The software has a feature to automatically choose the text segments (typically one or two sentences) related to the key words chosen (e. g. , hearing loss) and build a text corpus. This method avoids the researcher bias in selecting the text segments. It is important to note that the expressions "hearing loss" or "hearing aids" were inside each of the text segments extracted. These subcorpuses with more directed text segments were used for all further analysis. In the next stage, cluster analysis was performed on data sets with the Reinert's method (i. e. , divisive hierarchical clustering) used for textual data analysis  (Ratinaud & Marchand, 2012; Reinert, 1983 Reinert, , 1990)). This method has been frequently used in French media studies. A recent comparison between the Reinert method and the latent Dirichlet allocation showed that topics from the Iramuteq software that use the Reinert method are more accurate  (Sbalchiero & Tuzzi, 2017).
Changes in time regarding the hearing aid cluster also indicate that the focus of different text patterns changes over time. These changes largely reflect development in the hearing aid field. Moreover, it is noteworthy that there were numerous commonalities in some clusters created automatically from the software based on the heterogeneity of the text data within each cluster. For example, Clusters 1 (signal processing), 7 (innovation), and (wireless and connectivity) in the hearing aids data corpus showed frequent commonalities as they came under the same node. However, these clusters may have been combined if the same data were analyzed using the qualitative content analysis, depending on the interpretation of the researcher. The current study results suggest that newspapers provide wider and more realistic portrayal of hearing loss  Table  7.
Study Limitations and Future Directions The current study helps develop new knowledge about hearing loss and hearing aids in the media. However, the study has a few limitations, and the results must be interpreted with caution. First, no database represents all newspapers. We selected the U. S. Major Dailies database by ProQuest to construct the text corpus as this has indexing of national, regional, and local newspapers. Hence, we believe that this database provides an accurate and credible representation of the U. S. newspaper media. However, it is important to be aware that using a different database may have possibly resulted in different findings. Also, the database identifies and returns searches of all the articles indexed in the database. Hence, some news outlets with multiple newspapers may have influenced the results by overrepresenting the articles published about a particular topic.
Moreover, in recent years, there has been increased attention paid to media outlets as the public's trust in the media is at question (The Guardian, 2017). In the current study, we conducted the analysis by using a data set that included all the newspapers that were available in the database and presented information about hearing loss and hearing aids. For this reason, our data do not provide insights about whether or not the media outlets have any influence on the kinds of information they present regarding hearing loss and hearing aids. Second, as discussed in the Method section, our search was limited to one key word (e. g. , hearing loss). Using other key words with similar meanings (e. g. , hearing impairment, hard of hearing) would give additional hits. We did conduct the search with different key words and combined the corpuses, which resulted in repetition of articles as some articles contained both key words (i.
Conclusions The current study examines how hearing loss and hearing aids are represented in the media, specifically in the U. S. newspaper media. The information in the U. S. newspapers about hearing loss was mainly related to consequences, early identification and diagnosis, health promotion and prevention, recreational noise exposure, prevalence, research and development, and cognitive hearing science. The information in the U. S. newspapers about hearing aids was mainly related to signal processing, insurance, prevalence, research and development, activities and relation, features to address background noise, innovation, and wireless and connectivity. Time series analysis of clusters in both hearing loss and hearing aids data sets indicated that the change in pattern of information presented in the newspaper media during 1990-2016 (e.
g. , Cluster 7 focusing on cognitive hearing science in a hearing loss data set emerging only since the year 2012 and growing rapidly). The text pattern analysis showed that the U. S. newspaper media focuses on a range of issues when considering hearing loss and hearing aids and that patterns or trends change over time. The study results can be helpful for hearing health care professionals to understand what presuppositions society, in general, may have as a result of the newspaper media's influence on societal perception and opinions. Hearing health care professionals should endeavor to play an active role in providing accurate and useful information to the media regarding important trends in hearing loss and hearing aids. equality and entitlement. New Zealand Journal of  Psychology, 35, 3-13.
First, although we included 20 common physical diseases in our study, this is by no means an exhaustive list of all physical conditions. Thus, the results may have differed if more diseases were included. Second, subjective concentration and memory complaints were assessed with a single question. Thus, it is possible that the association between multimorbidity and SCC observed in our study may have differed with the use of a more complete measure. However, there are no standardized measures to capture SCC, and the use of a single question to assess SCC is common  4. Furthermore, the prevalence of SCC in our study was within previously reported ranges  3, 55, 56. Third, although the accuracy of patients' self-reports of diagnoses has been previously reported to be satisfactory  57, 58  , the use of self-reported data may have biased our findings.
Methods Study participants. This study used data from 7403 people who participated in the 2007 Adult Psychiatric Morbidity Survey (APMS). Full details of the survey have been published elsewhere  61. Briefly, this was a nationally representative survey of the English adult population (aged ≥16 years) living in private households. The National Center for Social Research and Leicester University undertook the survey fieldwork in October 2006 to December 2007 using a multistage stratified probability sampling design where the sampling frame consisted of the small user postcode address file (a listing of all postal delivery points), while the primary sampling units were postcode sectors. Participant information was obtained through face-to-face interviews where some of the questionnaire items were self-completed (with the use of a computer).
The interviewer was instructed to conduct a short proxy interview if the selected respondent was not capable of taking part in the survey due to health-related illness, stay in hospital, cognitive impairment, or mental incapacity. The data for these proxy interviews were not included in the publically available dataset. Sampling weights were constructed to account for non-response and the probability of being selected so that the sample was representative of the English adult household population. The survey response rate was 57%. Ethical permission for the study was obtained from the Royal Free Hospital and Medical School Research Ethics Committee. All participants provided informed consent before their inclusion. All methods were performed in accordance with the relevant guidelines and regulations.
Measures. Physical multimorbidity. We included data on all 20 physical conditions for which data were available in the APMS  62. These physical conditions were allergy, arthritis, asthma, bladder problems/incontinence, bone/back/joint/muscle problems, bowel/colon problems, bronchitis/emphysema, cancer, cataract/eyesight problems, diabetes, ear/hearing problems, epilepsy, heart attack/angina, high blood pressure, infectious disease, liver problems, migraine, skin problems, stomach ulcer or other digestive problems, and stroke. These 20 diseases are highly prevalent in the general population and are known to have a high burden at the population level. Participants self-reported whether they ever had pre-specified doctor diagnosed conditions since the age of 16. Specifically, the participant was presented with a card with the names of the health conditions and asked whether he or she ever had any of them since the age of 16.
Library preparation for Illumina technology Genome sequencing was performed using the instrument Illumina MiSeq. Libraries of genomic DNA were prepared using the Nextera DNA Library Preparation Kit as recommended by the manufacturer (Illumina). The sequencing reaction was performed using the MiSeq Reagent Kit v2 (300-cycles), paired-end reads of 150 nt × 2 (Illumina). The assembly of Moumouvirus B australiensis genome sequence was performed on one SMRT cell of Pacbio data using the HGAP workflow  [37]  from the SMRT analysis framework version 2. 3. 0 with default parameters, resulting in 84,565 corrected reads. The MinION library was sequenced for 1 h on a MinION Mk1B flowcell (Oxford Nanopore Technologies), generating ~220 Mb of sequence data. Basecalling was performed using the 1D Basecalling for FLO-MIN106 450 bps r1.
For Moumouvirus B maliensis, for which long reads were not available, the assembly was performed using Illumina paired-end reads and Spades (version 3. 12. 0) with the following parameters: careful, no reads correction, k = 33, 55, 77, 99, 127. Gene annotation of genomic sequences was done using Augustus  [39]  trained on already published members of the subfamily gene sets. The tRNAs were searched using tRNAscan-SE  [40]  with default parameters. Functional annotation of predicted protein-coding genes was done using homology-based sequence searches (BlastP against the NR database  [41]  and search for conserved domains using the Batch CD-Search tool  [42] ).
Phylogenetic analyses The phylogenetic tree of the transpovirons (Fig. 2 ) was computed using a concatenation of the three conserved genes (orthologous to mvtv_2, mvtv_6 and mvtv_7, see Fig. 2 ) using the optimal model "LG + G" selected by Prottest  [43]. The phylogeny of the giant viruses (including Megavirus C vitis, Moumouvirus B australiensis and Moumouvirus B maliensis, Fig. S2 ) was performed on a concatenation of the single copy genes shared by all mimiviruses. For that we first predicted the genes using CompareM (https://github. com/dparks1134/CompareM) and clustered them with MCL  [44] , resulting in 367 conserved genes, and selected among them the 197 present in exactly one copy per virus. Next, the sequences were aligned using Mcoffee  [45]  and concatenated. Finally, the phylogenetic tree produced from the resulting superalignment was based on the optimal "VT" model selected by Prottest  [43].
Characterization of virion proteomes by data dependent acquisition For proteomic analysis the virions pellets were resuspended in Tris HCl 40 mM pH 7. 5, 60 mM DTT, 2% SDS and incubated 10 min at 95 °C. The protein concentration of the lysates were measured using the 660 nm Protein Assay Reagent appended with Ionic Detergent Compatibility Reagent (Thermo Scientific) and 4 µg of proteins were analyzed as previously described  [12]. Two replicates were analyzed per sample, except for Z. vitis purified from M B. maliensis and Z. vitis purified from M C. chilensis containing matv. Peptides and proteins were identified and quantified using MaxQuant software (version1. 6. 2. 10)  [47]. Spectra were searched against the corresponding Megavirus C chilensis, Megavirus C vitis, Moumouvirus B australiensis, Moumouvirus B maliensis, Zamilon vitis, mvtv, matv and Acanthamoeba castellanii protein sequence databases and the frequently observed contaminants database embedded in MaxQuant.