dataesr/scientific-paragraphs-categorization huggingface dataset:
harvard. edu/research/mediacloud). See Supplementary Note 2 for additional details on the data collection. We chose to set the upper bound for the MC data collection as October 2016 so to avoid confounding the deluge of CC articles related to US elections and subsequent cabinet and other government administration appointments. One clear limitation of our MC dataset is that it is biased toward English-language content (98. 6% of articles analyzed are classified as English language by MC); however, since the discourse on CC began in the scientific domain, where English is also the de facto communication language, we do not believe this regional bias significantly affects our comparative results within the domain of English-based science and media content. We then refined the dataset by applying the following media article disambiguation method.
Upon close inspection of individual MC article metadata, we found that a significant number of articles indexed by MC refer to the same media article. We found this to be particularly common among the select-30 media sources included in our content analysis, in which the same article may have multiple different URLs, representing an array of hyperlinks from different facets of their website-e. g. , blog section, RSS feed section, and front page-to a common media article. As such, the initial set of 121,729 MC articles requires an additional merging procedure in order to avoid overcounting, resulting in a final dataset of 102,250 unique MC articles. While We addressed this redundancy by merging articles with the same MC media source and similar title into a single article instance so that article counts for sources and individuals are not systematically inflated.
We determine whether two titles are similar by calculating the Damerau-Levenshtein edit distance D jk between the title T j and T k. Titles that are similar have small D jk , meaning that a small number of character insertions, deletions, and swaps can convert T j and T k , or vice versa since it is a symmetric measure. between all pairs of MC articles from the same media source, which refined the total dataset size from 121,729 to 102,250 MC articles, a 16% reduction. We observed similar reduction levels for the set of articles belonging to each CCC and CCS group. CC publication data. We manually collected a dataset of 198,789 articles from 1900 to 2016 derived from the WOS Core Collection database (https://apps. webofknowledge. com/) using the search query (Climate NEAR/5 Change OR Global NEAR/5 Warming OR Climatology OR Climate NEAR/5 Model OR Climate NEAR/5 Extreme) AND (Climate); this query identifies a broad set of research streams relating to CC, while reducing the inclusion of more general meteorology and environmental research that refers to climate but not in the particular context of CC.
Supplementary Fig. 1a  shows the topical space of this WOS dataset on CC, confirming that our query captured the broad range of publications associated with the highly multi-disciplinary domain of CC research. Selection of contrarians (CCC). We compiled a list of 386 contrarians by merging three overlapping name lists obtained from three public sources. The first source is the list of former speakers at The Heartland Institute ICCC conference (http:// climateconferences. heartland. org/speakers/) over the period 2008-present, providing a representative sample across time; the second source is the list of individuals profiled by the DeSmogblog project; and the third source is drawn from the list of lead authors of the most recent 2015 NIPCC report (the principal summary of CC denial argumentation produced in conjunction with The Heartland Institute, http://climatechangereconsidered.
). We ranked individuals' publication profiles according to the net citations C i ¼ P i2p c p calculated by summing individual article citation totals (c p ) for only the individual articles (indexed by p) included within our WOS CC dataset. In this way, the CCS group is comprised of the 386 most-cited CC scientists, based solely on their CC research. Received: 15 March 2018 Accepted: 28 March 2019 Published online: 13 August 2019 UC Merced Center for Climate Communication and UC Merced Senate Faculty award for support.
403-19). All participants gave written informed consent. Visual stimuli. 128 virtual male bodies were prepared by using Poser© Pro, a software that creates avatars. We manipulated muscles, thinness, and heaviness of 4 models (Diego, Marcus, Tomo, and Ryan). We then asked an independent group of 16 randomly selected, exclusively heterosexual (Kinsey = 0) female participants (mean age = 27. 25, SD = 3. 09) to rate the models according to both liking ("how much do you like it?") and beauty ("how beautiful is it?") on a 1 ("not at all") to 7 ("very much") Likert-type response scale. Because the two measures were highly correlated (r = 0. 99, P < 0. 001), we averaged them in a unique attractiveness value. We then excluded 20 images that had mean ratings approaching the minimum possible rating (i.
A Siemens Allegra (Siemens Medical Systems, Erlangen, Germany) operating at 3T and equipped for echo-planar imaging (EPI) acquired functional magnetic resonance (MR) images. A quadrature volume head coil was used for radio-frequency transmission and reception. Head movements were minimized by mild restraint and cushioning. Thirty-two slices of functional MR images were acquired using blood oxygenation level-dependent imaging (3. 0 × 3. 0 × 2. 5-mm thick, 50% distance factor, TR = 2. 08 s, TE = 30 ms) covering the entire cortex. fMRI preprocessing. We used the statistical parametric mapping package SPM8 implemented in MATLAB (v 7. 1, The MathWorks, Natick, MA) for data preprocessing and statistical analyses. We acquired 1,960 fMRI volumes for each participant, 392 for each of the five functional runs.
The first four image volumes of each run were used for stabilizing longitudinal magnetization and were then discarded from the analysis. Preprocessing included rigid-body transformation (realignment) and slice timing to correct for head movement and slice acquisition delay. Preprocessed data were reviewed for motion using custom software from the Massachusetts Institute of Technology (http://web. mit. edu/swg/software. htm). Functional data were subjected to artifact detection if the derivative of the composite motion exceeded 1. 5 mm in any direction. Outlier scans were then modeled in the single-subject General Linear Model by including a single regressor for each outlier scan, with 1 for the outlier and zeros elsewhere. In order to model motion-related noise and spikes in the data, we included the derivatives of the six motion parameters as well as the composite motion parameter in the single-subject GLM  32.
Additionally, we saved the number of outlier scans and the Stimulus Motion Correlation (SMC) for each participant in each condition and checked whether there was a difference between the two groups. To rule out any motion-related group difference we ran a set of unpaired t tests on SCM that showed no statistically significant effect (Ts(30) < 1. 19, Ps > 0. 24). Slice-acquisition delays were corrected using the middle slice as a reference. All images were normalized to the standard SPM8 EPI template, resampled to 2-mm isotropic voxel size, and spatially smoothed using an isotropic Gaussian kernel of 8-mm FWHM. Statistical inference was based on a random effects approach  33. fMRI data analysis. First level analyses. The conditions of crucial interest were drawn from the social influence event-of participants being shown the putative mean rating of the fictitious gay group.
They were coded as Upward, Downward or Agree conditions depending on whether the mean attractiveness rating from the putative gay sample was higher, lower or identical to the one provided by the participants. For each participant, a contrast image was estimated from the following comparison: Disagree (mean of Upward and Downward social influence) vs. Agree condition. This contrast sought to ascertain any neural modulation induced by the Social influence of the minority (homo or bisexual) on the majority (heterosexual) group. Furthermore, for each participant's first rating, data were best fit at each voxel by convolving the hemodynamic response function with the time courses of the following conditions (whose appearance was modeled as an onset with a duration of 0 ms): high rating (7-9), low rating (1-3) and middle rating (4-6).
To this purpose, we used the R package lme4 ver. 1. 1-5  37  and modeled also the random slopes for the first rating and the social influence in order to have a better control of the Type I error, as recommended by Barr and colleagues  38. Furthermore, we controlled for Age. We then tested for fixed effects using a Type III Wald χ 2 Test through the Anova function in car  39. In order to provide a measure of the effect size, we refit the variables using the standardize function from the r package arm  40  and report the β coefficients. The following formula describes the model that was tested using the Wilkinson notation (ID stands for the effect of the participant): Delta ~ (1 | ID) + Age + Social influence + Group + First Rating + (0 + Social influence | ID) + (0 + First Rating | ID) + First Rating: Group.
We tested whether the difference in explicit prejudice could be related to the level of conformity. We found that MHS is not predictive of conformity behavior, when taking the regression towards the mean into account, as the interaction between MHS and Pressure did not reach statistical significance (β = 0. 09, χ 2 (1) = 0. 57, P = 0. 45). We tested the same hypothesis for the IAT, and the interaction between the IAT D scores and Pressure did not reach statistical significance (β = -0. 06, χ 2 (1) = 0. 24, P = 0. 62). To rule out the possibility that the two groups differed in their tendency to conformity in a non-specific way, we analyzed the data from the painting beauty ratings task (i. e. , the familiarization task) of 30 participants (we lost the data of two participants, one from each group) by means of an LMM model with only the random intercept, given the small number of observations per participant.
Acknowledgements Funded by the  Italian Ministry University  and research,  PRIN grant  ( Progetti di Ricerca di Rilevante Interesse Nazionale , Edit. 2015, Prot. 20159CZFJK). We thank  Nicola Nardelli  for his help in a preliminary phase of the study,  Adriano Acciarino  for his help in preparing the stimuli and  Scott Fairhall  for his precious feedback on an early version of the draft.
Health of migrant is a widely studied topic. It has been argued that migrant health may deteriorate over time. Though migrants are 'a hard to reach' population in survey data, this paper builds on a unique dataset provided by Médecins du Monde from five countries. We study self perceived health (SPH) in connection with socio-economic and demographic factors and length of stay. Results show different results for men and women. Asylum seekers compared to other documented migrants have a worse health. Migrants with better living conditions tend to be in better health. Employment and stable accommodation has a positive effect on SPH. Women from poorer countries have a better physical SPH after 3 months of residing in the host country. This paper contributes widely to knowledge of health of migrants.
MdM programmes are run predominately by volunteers (90% are volunteers and the remaining 10% are paid staff, of which around 90% are health professionals -including nurses, general practitioners, midwives, dentists, medical specialists, psychologists -and 10% are social workers, support workers, mediators, translators). The MdM International Network has developed a quantitative and qualitative information system that includes systematic patient data collection and annual statistical analysis, narrative patient testimonies, de jure and de facto legal analysis of healthcare systems, as well as identification of best practices. For this analysis we based our sample on total population seen by MdM. In the end we included 1356 adult migrants who consulted an MdM volunteer at one of the free clinics run by MdM or its partners in 5 countries for the first time in 2014.
Data collection Data was collected by MdM volunteers (doctors, nurses, social workers) through a medical and social questionnaire administered to every new adult patient. The medical questionnaire collected information on perceived health, vaccination, pregnancy and contraception, experiences of violence, history of HBV, HBC and HIV testing, reasons for consultation, and diagnoses at the end of the consultation. The social questionnaire collected information on housing conditions, occupation and resources, administrative situation, health coverage and obstacles to accessing healthcare. No ethics committee approval was required for this study as it uses socio-medical data routinely collected by health professionals. The medical records used in the study were collected and archived under the supervision of health professionals and according to the national laws and regulations of each country.
Statistical analysis Since self-perceived health is known to differ between men and women  [18, 19]  although it should be noted that very few studies have analysed migrant health in particular by gender  [20]  -all of our data has been analysed by gender. Firstly, we estimated a logistic regression model which included demographics (age in quartiles, country of residence, i. e. where people were interviewed and region of origin), migration status (residence status and length of stay in the host country) and SES (housing conditions, income satisfaction, job, health coverage). Secondly, we created a binary variable indicating whether or not the person is from a wealthy country (HDI above 0. 5), using the cut-off point of 0. 5 following Chaix et al. [17]. We tested whether the HDI of the country of origin is linked to our outcomes in men and women (see full results online), then tested whether the effect of length of stay in the host country modified these associations.
Acknowledgements This manuscript was written by  Bruce Edmonds  and  Christophe le Page , with suggestions and additions by the other authors. The authors wish to thank all those with whom they have discussed these issues, who are far too numerous for us to remember. However, these do include  Scott Moss ,  Alan McKane ,  Juliette Rouchier ,  David Hales ,  Guillaume De  uant,  Mario Paolucci ,  Rosaria Conte ,  Eckhart Arnold  and  Dunja Šešelja. Also all those at the workshop on Validation for Social Simulation held in Manchester in the summer of (including:  Nigel Gilbert ,  Mike Batty ,  Je Johnson , and  Rob Axtell ) whose contributions and discussions helped formulate these ideas. If you are not on this list, you should have argued harder. The manuscript has been handled by the former journal editor,  Nigel Gilbert.
Results and Discussion Monitoring Results. Information sources: The entire airborne concentration dataset and deposition dataset are available as SI Appendix, Tables S1-S4 and were mainly compiled through Ro5 exchanges, personal exchange, and data already published. Valuable information are also available on the Roshydromet website (6, 7), on the Typhoon Association website  (6) , and on the website of the Unified State Automated Monitoring System of the Radiation Situation in the Russian Federation  (8). Data from the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO)  (9)  are not part of the dataset except for those already published  (10). The International Monitoring System (IMS) data supporting the CTBTO (9) are available directly from the CTBTO upon request and signing a confidentiality agreement to access the virtual Data Exploitation Platform.
This study provides 106 Ru observations (>1,120 data points related to airborne activity and about 200 data points for deposited contamination, from about 330 sampling locations) that can be used for the purpose of atmospheric dispersion and deposition model validation. With the exception of the westernmost parts of Europe, most monitoring stations reported detections of 106 Ru in the range between some tenths μBq•m -3 meter and more than a 100 mBq•m -3. Fig. 1  illustrates the maximum activity levels per country. Activity concentrations in the millibecquerel per cubic meter range were reported between September 29, 2017 and October 7, 2017, exhibiting a short build-up and rapid decline behavior. The last traces of the plume (microbecquerel per cubic meter range) were measured in sampling periods ending between October 12, 2017 and the end of October by laboratories equipped with high-volume samplers and low-level γ-ray spectrometry.
106 Ru is a Significance A massive atmospheric release of radioactive 106 Ru occurred in Eurasia in 2017, which must have been caused by a sizeable, yet undeclared nuclear accident. This work presents the most compelling monitoring dataset of this release, comprising 1,100 atmospheric and 200 deposition data points from the Eurasian region. The data suggest a release from a nuclear reprocessing facility located in the Southern Urals, possibly from the Mayak nuclear complex. A release from a crashed satellite as well as a release on Romanian territory (despite high activity concentrations) can be excluded. The model age of the radioruthenium supports the hypothesis that fuel was reprocessed ≤2 years after discharge, possibly for the production of a high-specific activity 144 Ce source for a neutrino experiment in Italy.
ACKNOWLEDGMENTS. We thank all data providers, among them the  Environmental Protection Agency (Lithuania)  and the  Ignalina Nuclear Power Plant Laboratory (Lithuania) ; the  National Oceanic and Atmospheric Administration Air Resources Laboratory , for the provision of the HYSPLIT model for back-trajectory calculations; and the  Comprehensive Nuclear-Test-Ban Treaty Organization  for providing analysis results in the framework of a virtual Data Exploration Centre agreement (https://www. ctbto. org/specials/vdec/). We acknowledge financial support by the  VolkswagenStiftung  (Az. 94403). A. W. acknowledges funding by the  Deutschen Bundesstiftung Umwelt. The views expressed in this study are those of the authors and do not necessarily reflect the views of the  Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization.
Furthermore, creating optimal connections between pairs of points is not necessarily optimal for the network as a whole. So-called Steiner points (extra nodes to be used as junctions) can be inserted in order to optimize the connectivity of the whole network, but applying this principle to cost surfaces is very challenging  (Frommer and Golden 2007; Verhagen et al. 2014). Sensitivity analysis and validation of the resulting networks would therefore seem an important issue, but best practices for assessing the realism of (re)constructed networks are still largely lacking  (Knappett 2013b; Groenhuijzen and Verhagen 2017). The realism of the (re)constructed networks is also influenced by the completeness of the dataset of places to connect. Lack of information on presence and dating of sites is a common feature in archaeology and may influence not just the network configuration, but also all resulting analyses.
Bevan and Wilson (2013)  and  Paliou and Bevan (2016)  approached this by adding simulated settlements in the modelled network, based on a prediction of suitable site locations. By repeating this procedure a large number of times, it could be assessed whether the resulting networks' characteristics were stable or not. Davies et al. (2014)  and  Groenhuijzen and Verhagen (2016)  used a similar approach. In their case the site dataset was assumed to be relatively complete but lacking in chronological precision, making it difficult to interpret the evolution of the network through time. By repeatedly taking subsamples of the dataset and creating networks from them, the robustness of the observed patterns could be established.
Validation of Pathway Models A fundamental issue for pathway modelling is validation of the modelling results. This is only partly a matter of having sufficiently detailed archaeological data to judge the realism of model outcomes. A side-by-side comparison of different modelling outcomes is currently often a very demanding exercise, even when the goals and approaches are fairly similar -see e. g. Verhagen et al. (2013)  on comparing space syntax and SNA, or  Gietl et al. (2008)  on comparing different LCP algorithms. In practice, therefore, the outcomes of single models are often presented as the only possible representation of the research results. Even while chronological uncertainty and incompleteness of datasets will always be part archaeological research practice, sensitivity analysis therefore needs to become part of the standard methodology for pathway and network modelling in order to assess the quality of the presented models (see  Bevan and Wilson 2013; Groenhuijzen and Verhagen 2017).
g. , reprogramming a robot' s actions. Lastly, the system should analyze data (e. g. , the user' s performance history and robot operation) recorded in structured forms and provide it to different parties. These requirements serve as guidelines and evaluation criteria for RET systems. Some of the system design principles used to obtain these requirements are 1) multilayered behavioral organization (for generating task-based and social behaviors), 2) personalization (for providing personalized interaction), and modularity (to help with applying the system to different scenarios and robot platforms  [12] ). During development of the DREAM project, we adopted some of these design principles to establish a supervised autonomous system for different tasks in autism therapy (see the "Supervised Autonomous System" section).
The head pose is calculated by an object pose-estimation method. Iris centers are localized by a hierarchical adaptive-convolution method [see the red dots in Figure  4  Human action recognition, i. e. , a child's actions during interaction, plays a key role in evaluating imitation tasks performed by the child. A novel skeleton joint descriptor that uses a 3D moving trend and geometry property is applied on skeleton data extracted from Kinect's depth sensors [Figure  4(b) ]  [14]. The descriptor is then used to recognize actions (e. g. , waving, touching the head with two hands, moving the arms to imitate an airplane, or covering the eyes) by a linear support vector machine (SVM) classification algorithm. Facial expression recognition provides an understanding of the child's emotions, e. g.
A blob-based Otsu object-detection method is first employed to detect the objects. Then, a Gaussian mixture probability hypothesis density tracker is used to effectively detect and track objects in real time, even when being occluded by hands [Figure  4 (c)]  [16]. Audio processing provides information for the robot to perform social attention tasks and evaluate the child's verbal response. Speech recognition and sound direction are based The elements a RET robotic system should consider for generating robot behaviors  [11]. month 2019 • IEEE RoBotICS & AUtomAtIon mAGAZInE • on Kinect's Software Development Kit. Voices from the therapist and the child are labeled by classifiers such as the Gaussian mixture model, and vector quantification in combination with mel-frequency cepstrum coefficients, and linear predictive coding features  [17].