dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007; Buchard et al. , 2015; Hammer et al. , 2016). -There is an important sample-to-sample variability in our dataset of MAE values for mineral dust aerosols. At 532 nm, our average MAE values are 34 ± 14 and 78 ± 70 m 2 g -1 in the PM 10. 6 and PM 2. 5 mass fractions, respectively. Figure  3 , showing the correlation with the estimated mass fraction of elemental iron and iron oxides, suggests that this variability could be related to the regional differences of the mineralogical composition of the parent soils. These observations lead to further conclusions. To start with, our study reinforces the need for regionally resolved representation of the lightabsorption properties of mineral dust in order to improve the representation of its effect on climate. As a matter of fact, the natural variability of the absorption properties that we obtain from our study is in the range 50-100 %, even when we limit ourselves to smaller spatial scales, for example those from northern Africa (samples from Libya, Algeria, Mali and Bodélé).
g. ,  Hsu et al. , 2004). However, such a quantitative relationship cannot be uniquely determined from these studies, including the present one, which use different ways of estimating elemental iron, iron oxides and the total dust mass. A more robust estimate should be obtained from the imaginary parts of the complex refractive indices associated with the measurements of absorption and their dependence on the mineralogical composition. Data availability. Experimental and processed data are immediately available upon request to the contact author. They will also soon be made available through the EROCHAMP-2020 data center. The Supplement related to this article is available online at https://doi. org/10. 5194/acp-17-7175-2017-supplement. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement no. 730997. It was supported by the  French national programme LEFE/INSU , by the  OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie)  through dedicated research funding, by the  CNRS-INSU  by supporting  CESAM  as national facility and by the project of the  TOSCA program  of the  CNES (Centre National des Études Spatiales). Claudia Di Biagio  was supported by the  CNRS  via the Labex L-IPSL. Meinrat O. Andreae was supported by funding from  King Saud University  and the  Max Planck Society. The mobility of researchers between Italy and France was supported by the  PICS programme MedMEx of the CNRS-INSU.
The absence of adequate normalized keywords for identifying specific professional contextual features of GP/FM is an impediment to proper indexing of grey literature in GP/FM. More than 50% of the scientific outputs of general practitioners at congresses are never published  [14, 15]. Since McWhinney's famous initiative FAMLI  [16, 17] , there is no knowledge management system dedicated to GP/FM grey literature (research which has not been published in academic journals, nor indexed by the major bibliographic databases)  [18]. Consequently, study reports, congress abstracts, or collections of dissertations are often not globally available to researchers and practitioners, due to this lack of indexation  [19]. For example, the Wonca Europe website maintains a database of more than 30 000 abstracts of conferences in GP/FM.
Starting point for the analysis of the congress abstracts were the original Q-Codes created by H. Lamberts. To identify what is really at stake and respect the bottom-up approach, only themes addressed by GPs during the conferences have been classified. The data was analysed in a grounded theory approach with Atlas. ti software (http://atlasti. com/). Each abstract is read and code attributed along the classification ICPC-2 for clinical aspects and with a Q-Code for emerging professional contextual aspects. By grouping these identified concepts and their Q-Codes into subcategories and categories, gradually the taxonomy (a hierarchical ontological structure) takes shape. Cimino's standard set of desiderata for terminological resources was applied  [23]. For a full description of this development, the reader is referred to a prior publication  [24].
The Q-Codes taxonomy has been developed from a strong empirical basis in a bottom-up approach, and applying terminological expertise and sound qualitative methods. To provide free access to the taxonomy, a database was published on the web, created with a standardized terminological structure and advanced semantic web techniques. Great effort was invested in linking and mapping the newly identified Q-Codes to concepts in existing terminologies, both linguistic (Babelnet) and conceptual (PubMed). Further ontological research is needed to determine whether the two main rules of taxonomical thinking have been respected: completeness (all identified) and exclusivity (one place for each concept)  [29]. A taxonomy is never complete and must evolve with the discipline. In this development process, we stopped at 182 Q-Codes, but future congress will certainly reveal new concepts.
Why Twitter? Twitter was selected as the social media platform for diffusing our campaign messages, as Twitter information is posted voluntarily and is in the public domain. Unlike other social media platforms, Twitter provides several application programming interfaces (APIs) that allow real-time access to vast amounts of content, thereby aiding our understanding of social media processes. Adoreboard, a University spin-out company, enabled access to Twitter streaming data, which are preprocessed to minimize "noise," and allow maximal recovery of textual information and user metadata. Thus, the captured data are cleaned by removing unwanted messages and irrelevant tweets, which constitute noise in the message corpus. We aimed to remove tweets that did not contain the relevant hashtags of the campaign.
Measures Twitter analytics for key search terms (Multimedia Appendix 1) related to skin cancer and care in the sun were collected both before and after the campaign (April 2015 and October 2015) to serve as a comparison with the control group to establish whether the campaign resulted in greater use of such search terms, in Northern Ireland, following the campaign. Throughout the campaign, social media analytics were collected from Twitter dashboards. Access to Twitter streaming data was also enabled by Adoreboard, a University spin-out company. The most commonly cited and readily available social media metrics collected are defined and described later  [17, 24, 31]. Such metrics may tell us the extent to which a message spreads by detailing the number of users who see it, who respond to it, or who subsequently share that message with their friends or followers.
Impressions: The number of views of a particular post from users who saw it appearing on their timeline or through search results. Engagements: The number of clicks on the message, the picture posted, or the number of people who actively engaged with a post including likes, comments, shares, and retweets. Engagement rate: The ratio of engagements to impressions. Likes: Posts can be endorsed by the friends or followers of users that post messages by "liking" them (alternatively known as "favorites" on Twitter). Shares: Similarly readers of a post or status update who found the message interesting could also rebroadcast them by simply sharing (or retweeting) them. Data related to social media user demographics are limited. Twitter provides limited public information about the profile of its users within the description field.
Data Analysis Focus group and workshop discussions were audio-recorded and transcribed verbatim and anonymized. Transcripts were read repeatedly, initial codes identified, and themes collated and analyzed using an "a priori" thematic "Framework" method to produce themes related to perspectives of professionals and users  [33]. Data were compared for the pre-and postintervention survey, including social media usage, demographics, and knowledge and attitudes toward UV exposure and skin cancer prevention. Descriptive statistics (frequencies) of responses to questions were tabulated, and cross-tabulations used to report responses to questions by gender, age, and other sociodemographic characteristics. Tests of significance were omitted due to the nature of the study and the appropriateness of applying such tests to feasibility studies  [34].
Measuring Twitter Analytics Twitter data, collected via a service provider (Adoreboard), allowed greater access to the Twitter Firehose compared with the public APIs. The retrieved tweets and metadata were used to tabulate information such as the impressions and engagements of each tweet and to establish the frequencies of hashtag use and message spread (retweets). Metadata fields were also searched for relevant search terms (Multimedia Appendix 1) for the pre-and postcampaign frequency comparison.
Determining the Appropriate Process Evaluation Measures and Access to Data for a Social Media Campaign This study utilized commonly cited and most readily available Twitter analytics to evaluate the campaign: impressions, engagements, likes, and shares. Although such markers are commonly used in the literature  [17, 24, 31] , one might contest their appropriateness as evaluation measures for a public health campaign  [64]. Although measures such as impressions are useful in determining how many users see a given message, and retweets in determining the number who share such messages with friends or followers, it may not be appropriate to infer specific meanings from such actions. Does liking or retweeting a message infer that the user supports the campaign message or wants their friends to be aware of such advice or will indeed take heed of the message and act on such advice-or example in our case-to apply sunscreen? It would be naive to infer that they are good barometers of impending behavioral change.
Moreover, although weather data was collected for Northern Ireland throughout the campaign period, stronger associations between weather and retweets emerged during Phase 2 of the campaign. This was at a time frame that was closer to the "peak" of summer in Northern Ireland. However, during the summer of 2015, Northern Ireland experienced one of the coldest, wettest summers in approximately 30 years, and so any conclusions must be tentative. Although this study identified the appropriateness of social media for a public health campaign, the challenge is to find how to transfer traditional evaluation principles into the world of social media. Innovative methods are emerging with regard to social media. An instrumental variable approach to study happiness and weather effects has also been reported  [67].
Twitter, as a vehicle for dissemination and as an opportunity to reach new audiences, is endorsed by The Centers for Disease Control and Prevention  [71] ; however, fundamental challenges remain. Although respondents to our Web-based surveys were similar to those to the household survey, Twitter users are not representative of the general offline population. There are also ethical and privacy issues surrounding social media and Twitter that have not yet been tackled head on by most public health agencies  [72]. For example, difficulties may arise in reporting content, as Tweets can be searched, thus increasing the potential for subjects to be identified  [73]. It is not clear to what extent publicly available social media data can be regarded as "public," and as such, "concerns over consent, privacy and anonymity do not disappear simply because subjects participate in Web-based social networks; rather, they become even more important"  [74, 75].
Acknowledgments The intervention was funded by the  Medical Research Council ,  Public Health Intervention Development scheme , and implemented in collaboration with the  Northern Ireland Public Health Agency  and the largest regional cancer charity in Northern Ireland,  Cancer Focus NI. This work was supported by the  Centre of Excellence for Public Health  ( Northern Ireland ), a  UKCRC Public Health Research Centre of Excellence. RFH was funded by a  Career Development Fellowship  from the  National Institute of Health Research (NIHR). Approval was sought and granted by the  Research Ethics Committee of the School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast ,  Northern Ireland  ( Ref 14/55 ).
Genome sequencing and assembly The genome of E. chiriqhucha str. N139 was generated using 454 technology (Table  2 ). A standard 454 Titanium library was constructed and sequenced, producing 664,086 reads, totaling 253. 9 Mb of data. Phred quality cut-off was set to 20. The 454 data was assembled with Newbler, version 2. 8 and  MIRA, version 3. 4 (Chevreux et al. , 2004). The GS De Novo Assembler GUI was used, parameters selected were minimum read length of 45 and output scaffolds file. The parameters used for the MIRA assembly were: number of passes of 1 and no uniform read distribution nor trimming of overhanging reads. Warning message for read names longer than 40 was deactivated. Both assemblies were merged using Minimus2, from the amos version 3. 1. 0, with assembly errors manually corrected.
Genome annotation Protein-coding genes, tRNAs, rRNAs and non-coding RNAs were identified using the annotation pipeline Prokka  (Seemann, 2014) , followed by annotation refinement with Inter-ProScan  (Quevillon et al. , 2005). Riboswitches were identified with the Infernal 1. 1 package  (Nawrocki & Eddy, 2013)  using the corresponding covariance models from the Rfam database  (Burge et al. , 2013). COGs were assigned by profile hidden Markov model (profile HMM) searches using the hmmsearch program of the HMMER3 package  (Mistry et al. , 2013). For every COG, a multiple sequence alignment of bona fide representative sequences were generated using the Muscle program  (Edgar, 2004) ; then, the corresponding Hidden Markov Model was built using the hmmerbuild program, also provided in the HMMER3 package  (Mistry et al.
, 2013). The cutoff E-value in the hmmsearch process varies importantly for every COG. For every one of the COG groups we have defined a high confidence cutoff E-value value defined as the highest E-value (smallest bit score) observed for the members of such COG. In any case, none of the COG cutoff E-values was greater than 1e-10. The resulting annotation was subjected to manual curation. Pathway Tools 13  (Karp et al. , 2009)  in combination with the BioCyc  (Caspi et al. , 2014)  and UNIPROT  (UniProt Consortium, 2015)  databases were used to infer the metabolic capacities of E. chiriqhucha str. N139. The curated model of E. chiriqhucha str. N139 can be provided upon request, and will be deposited in the BioCyc database.
The circular plot was done with Circos software  (Krzywinski et al. , 2009). The Exiguobacterium strain N139 belongs to the chiriqhucha species along with E. pavilionensis str. RW-2 and Exiguobacterium sp. GIC31 A phylogenomic reconstruction (Fig. 1A ) placed the strain N139 as most similar to Exiguobacterium str. GIC31  (Vishnivetskaya et al. , 2014)  as well as to E. pavilionensis str. RW-2  (White III, Grassa & Suttle, 2013). ANI and AAI calculations of all clade II Exiguobacterium strains were performed and compared to our N139 strain, suggesting that E. pavilionensis str. RW-2, Exiguobacterium sp. GIC31 and this N139 strain belong to the same species since they share ANI values above 97% (Table  S2 )  (Goris et al. , 2007). Typically, the ANI values between genomes of the same species are above 95% (e.
DESCRIPTION OF EXIGUOBACTERIUM CHIRIQHUCHA SP. NOV. Exiguobacterium chiriqhucha (chi. ri. qhu. cha. (/ i ri ku t a/)) Quechua. Adj. chiri: cold, freezing; Quechua. Noun. qhucha: lake, pond. chiriqhucha of or belonging to a cold lake, referring to the common habitat of these three species). Members of the species Exiguobacterium chiriqhucha; inhabitants of freshwater ponds, saline ponds; distinguishable by their 16S rRNA sequences; accession numbers are: JMEH00000000 for the str. N139 genome, ATCL00000000 for the RW-2 genome  (White III, Grassa & Suttle, 2013)  and JNIP00000000 for the GIC31 genome  (Vishnivetskaya et al. , 2014). The three strains that so far comprise this species form orange shiny colonies and are Gram-positive, rod-shaped, facultative anaerobes and motile via peritrichous flagella  (Miteva, Sheridan & Brenchley, 2004; Vishnivetskaya, Kathariou & Tiedje, 2009; White III, Grassa & Suttle, 2013).
Journal of Bacteriology 194: 6689-6690 DOI 10. 1128/JB. 01791-12. Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, Holland TA, Keseler IM, Kothari A, Kubo A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S, Subhraveti P ,  Weaver DS , Weerasinghe D, Zhang P, Karp PD. 2014. The MetaCyc database of metabolic pathways and enzymes and the  BioCyc collection of Pathway/Genome Databases. Nucleic Acids Research 42 : 459-471 DOI 10. 1093/nar/gkt1103. Chevreux B, Pfisterer T,  Drescher B ,  Driesel AJ ,  Müller WEG , Wetter T, Suhai S. 2004. Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Research 14: 1147-1159 DOI 10. 1101/gr. 1917404. Crapart S, Fardeau ML, Cayol JL, Thomas P, Sery C, Ollivier B, Combet-Blanc  Y.
INTRODUCTION This paper presents CfA SN II light curves and spectra, all of which are publicly available from the journal, the CfA SN Group website 1 and The Open Supernova Catalog. 2 It is our hope that these data will be of use to the broader SN community for use in SN II analysis and cosmological calculations. Massive stars (M 8M ) end their lives as core-collapse supernovae (CCSN). Those that have retained a large portion of their hydrogen envelope are known as Type II SN, with spectra dominated by Balmer features. Those that have lost their hydrogen envelope and have no Balmer lines but do have helium features are known as Type Ib. Finally, those that have also lost much or all of their helium envelope and show no helium spectral features are known as Type Ic. For reviews of these classifications, see, for example,  Gal-Yam (2016)  and the introductions of  Smartt et al.
The potential cosmological use of SN II was pioneered by  Kirshner & Kwan (1974 , 1975)  with the expanding photospheric method for measuring distances. Schmidt, Kirshner & Eastman (1992)  applied this method to 10 SN II to calculate their distances and a value for the Hubble constant. Schmidt et al. (1994)  improved their method and applied it to 18 SN II to find a Hubble constant of H o = 73 ± 6(stat) ± 7(syst) km/s/Mpc, consistent within the error bars with more recent measurements such as the SN Ia and Cepheid-based value of H o = 73. 24 ± 1. 74 km/s/Mpc from  Riess et al. (2016)  and the P lanck cosmic microwave background radiation-based value of H o = 67. 8 ± 0. 9 km/s/Mpc from the Planck Collaboration (2016). More recently, several optical and NIR SN II data sets have been published while other unpublished data sets have been used for analysis or cosmological applications.
On a smaller sample of 61 SN II and using spectroscopic information, de Jaeger et al. (  2017 ) measured a dispersion of 0. 27 mag. Finally,  Gall et al. (2017)  presented light curves and spectra of nine SN IIP/L in the redshift range of 0. 045 ≤ z ≤ 0. 335 and combined them with data from the literature to update previous EPM and SCM Hubble diagrams. An interesting finding is that their three SN IIL are indistinguishable from their SN IIP in both the EPM and SCM Hubble diagrams. Larger samples are needed to confirm this but it suggests that SN IIL may be useful cosmological distance indicators as well. Given their higher luminosity than SN IIP, this could make SN IIL easier to find at higher redshifts, assuming a sufficient observing cadence is used to account for their faster decline. The Harvard-Smithsonian Center for Astrophysics (CfA) Supernova Group has been a source of data for nearby SN since 1993.
(2014)  and 60 SN II/IIn/IIb/Ibn light curves are presented in this work. Two additional NIR light curves have been published elsewhere: SN 2010jl  (Fransson et al. 2014 ) and SN 2011dh  (Marion et al. 2014). One SN IIb NIR light curve was produced after  Bianco et al. (2014)  and it is presented here. Additionally, optical light curves, spectra and analysis of SN IIP 2005cs and 2006bp were presented in  Dessart et al. (2008). Modjaz et al. (2014)  presented and analyzed optical spectra of 73 stripped-envelope CCSN while CfA SN II spectra for 47 of the 60 SN from the current paper are presented here. Finally, the CfA5 data set is currently being produced and will include optical SN light curves of all types taken after the CfA4 era (which included SN discovered up to mid-2010) plus any earlier ones that were missing calibration or host-galaxy subtraction images when the CfA4 light curves were created.
However, the CfA3-era light curves did not have multiple host galaxy subtractions and thus their uncertainty consists only of the photometric pipeline uncertainty. To ensure that the CfA3-era and CfA4-era SN II light curve uncertainties are comparable, we chose to only use the median of the photometric pipeline uncertainties  Note-Only a portion is shown here for guidance regarding its form and content. This table is available in its entirety in a machine-readable form in the online journal. It is also available at the CfA Web site: www. cfa. harvard. edu/supernova/index. html Note-This table presents the CfA natural-system SN II photometry. N host is the number of hostgalaxy images subtracted from the same data image. N host =0 means no host-galaxy subtraction was performed and the SN was sufficiently removed from the host galaxy.
σ is the same as the light curve uncertainty as used in  Hicken et al. (2009)  and is the same as σ pipe in  Hicken et al. (2012). The penultimate column lists during which light curve production campaign that data point was produced: CfA3 or CfA4. The last column lists which camera the SN data was acquired with. For CfA4 data, a 1 or 2 is appended to 'KEP' to indicate if it was taken during period 1 or 2 and which set of natural-system bandpasses should be used. Only the first two nights in each band of two SN (one from CfA3 and one from CfA4) are shown here for guidance regarding its form and content. This table is available in its entirety in a machine-readable form in the online journal. It is also available at the CfA Web site, as are the natural-system passbands: www. cfa. harvard. edu/supernova/index.
html DEC(J2000) V N V U -B N U B -V N B V -r N r V -i N i for the CfA4-era SN II light curves in this work (as opposed to adding it in quadrature to the standard deviation of the multiple subtraction light curve values for a given data point). In Table  6  we present the PAIRITEL NIR light curves. Figures  1  and 2  show two examples of CfA SN II light curves: one optical (SN 2008bn) and one with both optical and NIR (SN 2008in). Figure  3  shows the spectral series of SN 2007pk, a IIn, and SN 2008bn, a IIP. The photometry and spectra are available from the journal, the CfA SN Group website 8  and The Open Supernova Catalog. 9 The natural system passbands mentioned in  Hicken et al. (2012)  are also available at our website. The period-1 passbands can be used for the CfA3 KeplerCam and the CfA4 period-1 natural system light curves while the period-2 passbands can be used for the CfA4 period-2 natural system light curves.