dataesr/scientific-paragraphs-categorization huggingface dataset:
"  14 In contrast, verbal and textual content are subject to localization strategies such as translation, enculturation, and recontextualization. Applying Limor Shifman's approach to Internet memes as data units that are created through the networking of individual users in the Web 2. 0, my discussion focuses on the three dimensions of Internet memes that creators can potentially imitate and rework: content, form, and stance (see fig. 2 ). The first dimension encompasses the messages, ideas, and ideologies communicated by a meme. The dimension of form focuses on the "physical incarnation of the message" and describes a specific Internet meme's visual, and possibly audible, sensory characteristics. 15 Drawing on Robert Englebretson' s notions of "stance" and "stancetaking," the analysis of the third dimension can decipher the self-positioning of an Internet meme's addresser at the communicative crossroads of Internet meme, linguistic code, addressees, and other potential addressers (or, Internet meme recreators).
material 1: Table  S1 ). For four species M. bidentata, M. parvula, M. sophorella and M. tibetiensis hosts remain unknown  (Kuznetzov and Tristan 1985; Noreika and Puplesis 1992; Bai 2013 ). Only one species has been recorded from the Nearctic Region, M. salicifoliella  (Chambers, 1872) , whose larvae mine leaves of Salix  (De Prins and De Prins 2015). During a DNA barcoding campaign of leaf-mining insects from Siberia carried out in 2011, we discovered a genetically divergent lineage of Micrurapteryx feeding on the Siberian peashrub Caragana arborescens (Fabaceae). Preliminary barcoding data showed pronounced divergence in the COI barcoding fragment from European specimens of M. gradatella. Examination of the genitalia revealed that it was clearly different from European M. gradatella. In their taxonomic review of the Palearctic Micrurapteryx,  Kuznetzov and Tristan (1985)  called M.
DNA sequence analysis Sequence data for the barcode fragment  (Hebert et al. 2003)  were collected to estimate the barcode gap between M. caraganella and the related species. In addition, we sequenced two nuclear genes: histone H3 and 28S rDNA (28S) for M. caraganella and M. gradatella as an independent source of data to confirm the large divergence observed in the barcode fragment between these two species. The primers used in both amplification and sequencing were LCO (5' GGT CAA CAA ATC ATA AAG ATA TTG G 3') and HCO (5' TAA ACT TCA GGG TGA CCA AAA AAT CA 3') for the COI gene  (Folmer et al. 1994 ); H3 F (5' ATG GCT CGT ACC AAG CAG ACG GC) and H3 R (5' ATA TCC TTG GGC ATG ATG GTG AC) for the H3 gene  (Colgan et al. 1998) ; and D1F (5' ACC CGC TGA ATT TAA GCA TAT) and D3R (5' TAG TTC ACC ATCTTT CGG GTC) for the 28S gene  (Lopez-Vaamonde et al.
2001). DNA from 22 specimens of M. caraganella, seven specimens of M. gradatella and one Parectopa ononidis was extracted, PCR amplified and sequenced at INRA (Orléans, France). DNA was extracted using NucleoSpin® tissue XS kit, Macherey-Nagel, Germany, according to the manufacturer's protocol. The COI barcoding fragment, 658 bp, was amplified via PCR at the standard conditions for the reaction. PCR products were purified using the NucleoSpin® Gel and PCR Clean-up kit Macherey-Nagel, Germany and sequenced by the Sanger method with Abi Prism® Big Dye®Terminator 3. 1cycle sequencing kit (25 cycles of 10 s at 96 °C, 5 s at 50 °C, 4 min at 60 °C). Sequencing was carried out using a 3500 ABI genetic analyzer. All sequences were aligned using CodonCode Aligner 3. 7. 1. (CodonCode Corporation). DNA for the remaining samples was extracted and barcoded at the Canadian Centre for DNA Barcoding (CCDB, Biodiversity Institute of Ontario, University of Guelph) using the standard high-throughput protocol described in deWaard et al.
(2008). In addition, 109 samples of North American species Micrurapteryx salicifoliella, M. occulta, and Parectopa robiniella, earlier barcoded by other colleagues, were also included in the analysis (Table  1 ). The resultant sequences, along with the voucher data, images, and trace files, are deposited in the Barcode of Life Data Systems (BOLD)  (Ratnasingham and Hebert 2007; www. barcodinglife. org) and the sequences were deposited in GenBank. All data are available through the following dataset (http://dx. doi. org/10. 5883/DS-MICRURA) Intra-and interspecific genetic distances were estimated using the Kimura 2-parameter model implemented within the analytical tools available in BOLD. We also used BOLD to obtain Barcode Index Numbers (BINs)  (Ratnasingham and Hebert 2013). A neighbor-joining (NJ) tree was constructed using MEGA 5.
2011). The relatively high level of DNA barcode divergence found between M. caraganella and M. gradatella contrasts with the limited differentiation in the two nuclear genes sequenced (i. e. H3 and 28S) (Table  2 ). The striking difference in the level of divergence between mitochondrial and nuclear genes could be caused by maternally inherited symbionts such as Wolbachia  (Kodandaramaiah et al. 2013). A study on Wolbachia infection of both species is needed to confirm the role of this endosymbiont on the levels of mitochondrial and nucleotide diversity observed. Host range in Micrurapteryx. The genus Micrurapteryx comprises species feeding on more than twenty different genera of legumes, and a host shift from Fabaceae to Salicaceae (Suppl. material 1: Table  S1 ). In North America M. occulta has been recorded on several different genera of Fabaceae hosts (Caragana, Lathyrus, Lupinus, Melilotus, Vicia) (see specimens examined in DS-MICRURA dataset and Suppl.
Specimens used for molecular analyses. Both the Process ID and Sample ID codes are unique identifiers linking the record in the BOLD database and the voucher specimen from which the sequence is derived. Additional collecting and specimen data are accessible in the BOLD dataset dx. doi. org/10. 5883/DS-MICRURA as well as GenBank (http://www. ncbi. nlm. nih. gov/genbank/). Where pertinent, genitalia preparation number and sex are given in square brackets in.
Acknowledgements We thank  Eugeniy Akulov  ( Krasnoyarsk, Russia ) for collecting and rearing some M. caraganella in Krasnoyarsk in 2014,  Leonid Krivobokov  ( Krasnoyarsk, Russia ) for identifying plant species from Siberia,  Margarita Ponomarenko  ( Vladivostok, Russia ) for her help with the search of some Russian literature and useful comments. We thank  Rodolphe Rougerie  ( Paris, France ) for insightful comments on the manuscript,  Liisa Vainio  ( Turku, Finland ) for allowing us to publish her photos of M. gradatella mines and  Markus J. Rantala  ( Turku, Finland ) for providing larval and pupal samples of M. gradatella for our examination. We similarly thank  Jeremy deWaard  ( Guelph, Canada ),  Peter Huemer  ( Innsbruck, Austria ) and  Eric LaGasa (Olympia, Washington, USA)  who kindly allowed us access to unpublished barcodes.
Funding for DNA barcoding and sequence analysis was partly provided by the  Government of Canada through Genome Canada  and the  Ontario Genomics Institute  in support of the  International Barcode of Life  project, and by  NSERC. Genetic analyses were also partly funded by  INRA ,  UR0633  Zoologie Forestière's core funding. Our work was also aided by the  BOLD  informatics platform whose development is funded by the  Ontario Ministry of Economic Development and Innovation. NK was supported by a fellowship of  LE STUDIUM ® , France  and the  Russian foundation for basic research  (grant N o  15-29-02645 ). Finally we thank  David Wagner ,  Camiel Doorenweerd  and the editor  Erik van Nieukerken  for their careful and detailed reviews which greatly improved our manuscript.
Geoengineering is typically defined as a techno-scientific response to climate change that differs from mitigation and adaptation, and that includes diverse individual technologies, which can be classified as either solar radiation management or carbon dioxide removal. We analyse the representation of geoengineering on Wikipedia as a way of opening up this dominating, if contested, model for further debate. We achieve this by contrasting the dominating model as presented in the encyclopaedic article texts with the patterns of hyper-link associations between the articles. Two datasets were created tracing the geoengineering construct on Wikipedia, shedding light on its boundary with its context, as well as on its internal structure. The analysis shows that the geoengineering category tends to be associated on Wikipedia primarily with atmospheric solar radiation management rather than land-based carbon dioxide removal type technologies.
The results support the notion that the dominant model of defining and classifying geoengineering technology has been beneficial for solar radiation management type technologies more than for land-based carbon dioxide removal ones. The article also demonstrates that controversy mapping with Wikipedia data affords analysis that can open up dominating definitions and classifications of technologies, and offer resistance to their frequent naturalising and decontextualising tendencies. This work is in line with recent work on digital sociology, but the article contributes a new methodology and reports on the first empirical application of controversy mapping using Wikipedia data to a technology.
, 2015; Yaneva, 2011). This article not only shares these two features but also innovates on the methodology of social cartography by focusing on a peculiar type of digital traces -the networks of labels and hyperlinks woven in a set of Wikipedia articles. While controversy mapping has extensively drawn on corpuses of scientometric references  (Callon et al. , 1986; Shwed and Bearman, 2010) , news articles  (Chateauraynaud, 2009) , websites  (Rogers, 2004)  and social media pages  (Niederer, 2013) , the potential of Wikipedia as a source of data to study socio-technical controversies has not been fully exploited. Currie (2012)  has studied debates over feminism on Wikipedia, but S&T boundary controversies have not been studied there. To open up the dominant model of definition and classification of geoengineering, this article also develops a new methodology that builds on a tension identified between two formal properties of Wikipedia.
It is this pre-formatted nature of the data that makes Wikipedia suitable for analysis of the structure of boundaries between issues  (Marres and Weltevrede, 2013). Whilst the encyclopaedic articles as such can be expected to support the dominant model of definition and classification of geoengineering in drawing neat boundaries around geoengineering and its component classes, the pattern of hyperlinks between articles might show where the controversial complexity overflows 2 the confines of the dominant model and different boundaries may become visible. We can use the tension between genre and medium to open up the dominating model. Geoengineering related articles on Wikipedia also link to other articles, which can be analysed as the context of geoengineering. Wikipedia data is therefore suitable for analysis of both internal and external boundaries.
Moreover, by studying Wikipedia as a network of links and labels, we can also analyse how the representation of geoengineering mobilises and is framed by notions belonging to a wide range of discursive fields (natural sciences, political debate, technical literature as well as legal and economic discourse). Analysis of such Wikipedia data is thus both contextual and heterogeneous. The tension between the genre and the medium allows us to analyse the contrast between the dominating, reductive model of definition and classification of geoengineering and a representational form that both reproduces and challenges this reduction, as well as offers scope for contextual and heterogeneous analysis. Therefore, we will in this article contrast the dominant model of definition and classification in the literature with the presentation of geoengineering on Wikipedia to determine whether the tension between genre and medium helps us open up the dominating model.
Methods The aim that guided the development of the approach to data collection and analysis was to be able to compare the dominant model of definition and classification of geoengineering with the representations of it on Wikipedia. Importantly, this included comparing both internal boundaries within the geoengineering construct (the main geoengineering article plus other articles with geoengineering content) and external boundaries between geoengineering and its context. We collated two datasets containing Wikipedia articles and the hyperlinks between them. We only considered links that were intentionally included by editors in the text of an article, and discarded links automatically generated by templates. The datasets were constructed to include articles focussing on geoengineering so as to analyse its internal structure, but also other related articles so as to explore the context of geoengineering on Wikipedia.
The first dataset 3 was generated to explore the boundary between geoengineering, mitigation and adaptation within the specific context of climate change. The dataset was therefore constructed to contain as many climate change related articles as possible. This was done through a combination of manual expert choice and by drawing on the category structure of Wikipedia (where editors indicate the relationship between articles in a hierarchical topic structure) through including articles in categories under the category of climate change. 4  This generated a climate change related network dataset of 1063 nodes (articles) and 6208 edges (hyperlinks). The second dataset 5 was generated to explore whether there was any other relevant contexts to geoengineering than climate change. We therefore started by manually selecting (drawing on our expertise and acquaintance with geoengineering literature) an initial set of articles that best reflects the dominating definition of geoengineering and enumerations of geoengineering technologies in the literature.
The hierarchical category structure is not suitable for this more openended search for contextual articles, and instead the dataset was then extended to include further articles that were closely associated (inter-linked) with the initial set. We chose articles that linked to or were linked from at least two articles in the initial set. This generated a geoengineering based network of 529 nodes (articles) and 8887 edges (hyperlinks). The analysis was conducted by comparing the dominant model of definition and classification of geoengineering with the representations of it on Wikipedia. We manually identified the geoengineering construct in the form of groups of articles in each network that most closely related to the dominant model (see Tables  1 to 3 ), and interpreted the complement of these as their context.
The datasets were rendered suitable for this using the GEPHI 6 network visualisation software  (Bastian and Heymann, 2009). The software draws closer the nodes representing articles that link to each other, using a so-called forcevector algorithm. In particular, we used the algorithm ForceAtlas 2, checking the option of LinLogMode option and setting gravity to zero. 7 Unlike scatterplots or geographical representation, such techniques do not rely on a pre-existing space. The nodes are not positioned according to pre-defined coordinates, or to a category to which they are deemed to belong. Rather, the graphs define a relational space. The algorithm produces this by simulating a system of physical forces: nodes are charged with a repulsive force driving them apart, while edges introduce an attractive force bounding nodes together  (Jacomy et al.
We primarily used visual analysis because, given the objectives of this article, the mathematical proof of our findings is less important than the insights that the visual examination of the networks offered to us. The continuous and debatable nature of the visual analysis of our networks has great value (in line with the tradition of 'exploratory data analysis',  Healy and Moody, 2014; Tukey, 1977). Precisely because it is questionable, it sparks questions and encourages us (and our readers) to reflect on the blurred and permeable boundaries of geoengineering. In addition to the visual analysis, metrics describing the structure of article inter-linkage were calculated. The two metrics of 'in-degree' and 'out-degree' represent the number of incoming and outgoing links for each article. In addition, articles are assigned a high 'page rank' when receiving many links from other articles, especially those that also receive many links  (Brin and Page, 1998).
Appropriately for a study of contested categories, we also used a metric of the controversiality of individual articles to help analyse the network structure. The h-index measures the depth and breadth of editors' discussions on the talk pages; an article has h-index N if at least N subthreads in its discussion tree reach depth N (i. e. , N levels of nested replies). For a more detailed description of the metric, see  Laniado et al. (2011). In the representation of the first, climate change dataset (Figures  2  and 3 ), node size is proportional to the h-index of the node. While the rankings produced by h-index and in-degree tend to overlap, as the most central concepts tend to also be the more controversial, they don't coincide fully. 9 For this article, we chose h-index instead of degree, which is usually used to represent node relevance in a network to highlight the controversiality of the articles.
See data in Tables  2  and 3. The core group includes SRM, space mirrors, ocean iron fertilisation, weather modification and other specific technologies, but also the topic of 'geoengineering' itself. This suggests that geoengineering is constructed by the editors of Wikipedia as a cohesive and distinct thematic entity in relation to climate change, although in a way that does not simply map onto the normal definitions. The core group is closer to the existence/ attribution and adaptation meta-categories than the mitigation one, and, notably, close to several articles relating to climate emergency  (Hulme, 2008; Markusson et al. , 2013) , which helps explain its controversiality. The second group (here called the 'land-based sequestration group') includes technologies like bioenergy with carbon dioxide capture and storage (BECCS), biochar, reforestation, and 'CDR' itself.
Discussion The heterogeneous analysis that is possible with Wikipedia data allows us to see geoengineering as not just a scientific category. This heterogeneity also allows us to analyse the way geoengineering is situated among a range of societal institutions, including media, industry and, marginally, the military. This result thus goes some way to bringing contingency and multiple contexts back into the analysis. The geoengineering structure found does also not respect the dominant model of classifying geoengineering technologies. The boundaries drawn on Wikipedia do not match the neat three-level hierarchy of geoengineering classification, and are instead replaced by a more complex nested topology. The zooming possible with Wikipedia data allows us to see a subtle structural layering, rather than a stark CDR-SRM dichotomy.
Conclusions The first of two main contributions of this article is the identification of an ambiguity between the dominating model for defining and classifying geoengineering and the tendency in representational practice to actually refer to core technologies more than land-based sequestration technologies. This analysis fills a gap in the literature with regard to how geoengineering is bounded and defined in representational practice. The identified ambiguity also matters politically, e. g. , for decisions on research funding and formulation of climate policy, and for facilitating debate about geoengineering. The second main contribution is that the article presents the first application of controversy mapping using Wikipedia data to an empirical case of S&T. This article contributes a demonstration of a new tool for the box of methods suitable for the study of controversial public representations of S&T, especially with regard to.
Only SVs tracked for >40 consecutive frames were considered. Live imaging TIRF microscopy. For live-cell TIRF microscopy, transfected cells were visualized on a TIRF microscope (Roper Technologies) equipped with an ILas 2 double laser illuminator (Roper Technologies), a CFI Apo TIRF 100× (1. 49-NA) objective (Nikon), and an Evolve512 delta EMC CD camera (Photometrics). Image acquisition was performed using Metamorph software (version 7. 7. 8; Molecular Devices). Cells were bathed in buffer A. sptPALM. Time-lapse TIRF movies were captured at 50 Hz (16,000 frames) at 37°C for both control and stimulated cells. BaCl 2 (2 mM) was added immediately before initiating image acquisition to stimulate the cells. For PALM, a 405-nm laser was used to photoactivate the cells expressing mEos2-tagged constructs, and a 561-nm laser was simultaneously used for excitation of the resulting photoconverted molecules.
PALM-Tracer, a custom-written program that runs as a plug-in in Metamorph software (Molecular Devices), was used to localize the molecules and quantify the dynamics of the proteins. The diffusion coefficient (D) distribution was sorted into two groups. The first group, composed of molecules with a D value <0. 0312 µm 2 /s, was referred to as immobile. The D threshold = 0. 0312 µm 2 /s was calculated as previously described  (Constals et al. , 2015). The second group was defined as the mobile fraction and composed of molecules with D values higher than 0. 0312 µm 2 /s. The color-coding for the superresolved images was performed using ImageJ. The color-coding of the trajectory maps are arbitrary units of 16 colors. The color gradient represents the time of the detection of the tracks in the video.
Single-molecule localization microscopy and data analysis Transfected DKD-PC12 cells expressing Munc18-1-mEos2 or Munc18-1 Δ317-333 -mEos2 were incubated in buffer A alone or stimulated with 2 mM Ba 2+ for 2 min. The cells were then fixed with 4% paraformaldehyde diluted in PBS for 45 min and washed in PBS. Tetraspeck beads (0. 1 µm; Invitrogen) were added before the acquisition of images on an ELY RA PS1 microscope (ZEI SS) equipped with a 100× objective (alpha Plan-Apochromat 100×/1. 46 oil-immersion) and an EMC CD camera. mEos2 molecules were simultaneously photoconverted with a 405-nm laser and excited using a 561-nm laser, as described earlier. For each dataset, 20,000 frames were acquired at a rate of 33 Hz, at which point the majority of mEos2-tagged molecules had been photoconverted.
The acquired time-lapse videos were processed to retrieve the coordinates of individual localizations using Zen software (ZEI SS). The datasets were corrected for x,y drift using the fluorescent Tetraspeck beads as fiducial markers, and fluorescent molecules imaged in multiple frames were compensated for by consolidating those that appeared within 1 frame and 1 pixel of one. Datasets were further filtered by excluding all points with a localization precision <10 nm or >40 nm. To examine the superresolved distribution of Munc18-1 on the plasma membrane, single-molecule localization images were reconstructed from the filtered coordinates with a pixel size of 10 nm and were used for further analysis. A custom-written program in Matlab (Mathworks) was used to quantify the spatial distribution and degree of clustering of Munc18 molecules through the use of an autocorrelation function  (Harper et al.