dataesr/scientific-paragraphs-categorization huggingface dataset:
Within each set of regression lines, the middle and outer lines relate to the median and 95% Bayesian credible intervals of the posterior predictive distribution, respectively (black set for areas all areas but Mbam; grey set for Mbam, the only forestsavanna mosaic area). Predictions were made assuming that nodule prevalence was based on a sample of 30 adult males. doi:10. 1371/journal. pntd. 0002168. g003 predictions for mf prevalence, based on nodule prevalence in a sample of 30 males from a hypothetical village outside the dataset (Figure  3 ). Mixed posterior predictive checks showed that the model fitted well to the data (Figure  4 ). Only three villages -all from different regions, and all with relatively low infection levels compared to other villages from the same region -deviated significantly from the model predictions.
LEC and WAS were funded by a research grant from the  African Programme for Onchocerciasis Control  (APOC, www. who. int/apoc) for a Health Impact Assessment of APOC ( CEV/APOC/241/08/TSA ). MGB and WAS acknowledge seed funding from the  Institute of Health Metrics and Evaluation (Seattle, USA , www. healthmetricsandevaluation. org/) as co-disease leaders for the project on global burden of onchocercal disease. SOH was funded by an  ESRC (Economics and Social Sciences Research Council, UK , www. esrc. ac. uk)PhD studentship. Surveys in Cameroon were performed with the financial supports from the  Special Programme for Research and Training in Tropical Diseases  (Vina; www. who. int/tdr), Helen Keller International  (Lekie; www. hki. org) and  Institut de Recherche pour le Developpement (Mbam and Faro ; www.
Although responses to these types of questions can provide useful feedback to observers and instructors, the data are observer dependent and cannot easily be standardized or compared across multiple classrooms (e. g. , all STEM courses at UBC or UMaine). Alternatively, structured protocols provide a common set of statements or codes to which the observers respond. Often, these protocols ask observers to make judgments about how well the teaching conforms to a specific standard. Examples of such protocols include the Inside the Classroom: Observation and Analytic Protocol  (Weiss et al. , 2003)  and the Reformed Teaching Observation Protocol (RTOP;  Sawada et al. , 2002). These protocols consist of statements that observers typically score on a Likert scale from "not at all" to "to a great extent" and contain such statements as: "The teacher had a solid grasp of the subject matter content inherent in the lesson" (from RTOP;  Sawada et al.
DEVELOPMENT The development of COPUS was an evolutionary process extending across more than 2 years, involving many iterations and extensive testing. It began at UBC, where science education specialists (SESs) who were working with science faculty on improving teaching  (Wieman et al. , 2010)  wanted to characterize what both the students and instructors were doing during class. The SESs began testing various existing protocols, including the TDOP, in different classes at UBC in late 2011 and early 2012. The original TDOP did not meet our needs (as described above), so we iteratively modified the protocol through nine different versions. These changes resulted in a format, procedure, data structure, and coding strategy that was easy to implement on paper or electronically and convenient for analysis and display.
The overall format of the observation protocol remained largely stable, but the categories and codes continued to evolve. During the Fall term of 2012, 16 SESs, who are highly trained and experienced classroom observers, used this evolving protocol to observe a variety of courses in singles, pairs, or trios across most of the departments in the UBC Faculty of Science (including the disciplines of biology, computer science, earth sciences, mathematics, physics, and statistics). We analyzed the SES generated observation data to identify coding disagreements and met with the SESs to discuss the evolving protocol and coding. These discussions covered observed behaviors they found difficult to code and/or hard to interpret, and other important elements of instructor or student behavior they felt were not being adequately captured.
Observers checked the instructor code "FUp: Follow-up" to describe this behavior but stated they occasionally forgot to also select the instructor code "PQ. " To compare observer reliability across all 25 codes in the COPUS protocol, we calculated Cohen's kappa IRR scores using SPSS (IBM, Armonk, NY). To compute the kappa values for each observer pair, we added up the total number of times: 1) both observers put a check in the same box, 2) neither observer put a check in the same box, 3) observer 1 put a check in a box when observer 2 did not, and 4) observer 2 put a check in a box when observer 1 did not. For example, at UBC, when looking at all 25 codes in the COPUS, one observer pair had the following results: 1) both observers put a check in 83 of the same boxes, 2) neither observer put a check in 524 of the boxes, 3) observer 1 marked six boxes when observer 2 did not, and 4) observer 2 marked 12 boxes that observer 1 did not.
Using data such as these, we computed the kappa score for each of the eight UBC and 23 UMaine pairs and report the average scores in Table  4. We also repeated this calculation using either the subset of 13 student or 12 instructor codes (Table  4 ). The average kappa scores ranged from 0. 79 to 0. 87 (Table  4 ). These are considered to be very high values for kappa and thus indicate good IRR  (Landis and Koch, 1977). Notably, the kappa values, as well as the Jaccard similarity scores, are comparably high for both UBC faculty and UMaine K-12 teacher observers, indicating that COPUS is reliable when used by observers with a range of backgrounds and 2 hours or fewer of training.
Faculty who teach these classes may benefit from professional development activities about how to design an effective active-learning classroom. In addition, the data can be analyzed for a subset of faculty members who are using active-learning strategies, such as asking clicker questions. Thirty-eight percent of UBC and 43% of the UMaine classes that were observed used clickers. However, student code prevalence in these classes show that not all faculty members used clicker questions accompanied by recommended strategies, such as peer discussion  (Mazur, 1997; Smith et al. , 2009 Smith et al. , , 2011 ; Figure  6 ). Faculty members who are not allowing time for peer discussion may benefit from professional development on how to integrate peer discussion into clicker questions.
DISCUSSION AND SUMMARY COPUS was developed because university observation programs needed a protocol to: 1) characterize the general state of teaching, 2) provide feedback to instructors who desired information about how they and their students were spending class time, and 3) identify faculty professional development needs. COPUS meets all of these goals by allowing observers with little observation protocol training and experience to reliably characterize what both faculty and students are doing in a classroom. There are several uses for COPUS data. On an individual level, faculty members can receive pie charts with their code prevalence results (examples in Figure  4 ). These results provide a nonthreatening way to help faculty members evaluate how they are spending their time. We discovered that faculty members often did not have a good sense of how much time they spent on different activities during class, and found COPUS data helpful.
In addition, faculty members can use COPUS data in their tenure and promotion documents to supplement their normal documentation, which typically includes student evaluation information and a written description of classroom practices. Having observation data gives faculty members substantially more information to report about their use of active-learning strategies than is usually the case. COPUS data can also be used to develop targeted professional development. For example, anonymized, aggregate COPUS data across all departments have been shared with the UMaine Center for Excellence in Teaching and Assessment, so workshops and extended mentoring opportunities can better target the needs of the faculty. One area in particular that will be addressed in an upcoming professional development workshop is using clickers in a way that promotes peer discussion.
The idea for this workshop came about as a result of the COPUS evidence showing the prevalence of UMaine STEM classes that were using clickers but allowing no or minimal time for recommended student peer discussions (Figure  6 ). Other planned uses for COPUS include carrying out systematic observations of all instructors in a department at UBC in order to characterize teaching practices. The information will be used with other measures to characterize current usage of research-based instructional practices across the department's courses and curriculum. In the end, the choice of observation protocol and strategy will depend on the needs of each unique situation. COPUS is easy to learn, characterizes nonjudgmentally what instructors and students are doing during a class, and provides data that can be useful for a wide range of applications, from improving an individual's teaching or a course to comparing practices longitudinally or across courses, departments, and institutions.
ACKNOWLEDGMENTS This work was supported at  UBC  through the  Carl Wieman Science Education Initiative  and by the  National Science Foundation  under grant # 0962805. We are grateful for the assistance of all of the  UBC  SESs who contributed to the development of the survey;  Lisa McDonnell  and  Bridgette Clarkston  for running the UBC training session;  MacKenzie Stetzer ,  Susan McKay ,  Erika Allison ,  Medea Steinman , and  Joanna Meyer  for helping to run the UMaine training session;  Jeremy Smith  for developing scripts to parse and analyze the data; the Maine K-12 teachers and  UBC faculty  volunteers who served as observers; and the faculty at  UBC  and  UMaine  who allowed their courses to be observed. Approval to observe classrooms and instruction at  UBC  and publish results of that work is provided to the  Carl Wieman Science Education Initiative  by the  University of British Columbia  under the policy on institutional research.
Patient data The viral kinetic data we analyzed are from eight treatment naive patients who were infected with HCV genotype 1 and treated with danoprevir monotherapy (200 mg tablets three times a day) for 14 days  [15]. Viral loads were measured post treatment initiation at hours 0 (baseline), 2, 4, 6, 8, 12, 16, 24 (day 1), 26, 28, 30, 48 (day 2), 52, 144 (day 6), 148, 192 (day 8), 196, 312 (day 13). The NS3 protease sequence was evaluated by population sequencing at days 0, 2, 6 and 13. Three patients 01-94AB, 03-94EZ, and 03-94SN were found to develop mutations conferring drug resistance, all of them at position R155K. The models we use to analyze the data assume that drug effectiveness is constant over the period of treatment. Hence these models are not suitable for analyzing patient data once drug resistance is apparent and we thus fit the data from these three patients until the time at which resistance was identified.
Analysis of viral decline data using the multiscale model We analyzed patient data using the long-term approximation of the multiscale model under therapy. The long-term approximation (Eq. 17) has 11 parameters, including the baseline viral load V 0 and the pharmacological delay t 0. We chose V 0 and t 0 to have the same values as determined for the biphasic model (Table  1 ). Because parameters that are not related to treatment should not depend upon the DAA being used, we chose m~1 d {1 , c~22:3 d {1 (corresponding to a half-life t 1=2 ~45 minutes), d~0:14 d {1 (corresponding to t 1=2 ~5 days), r~8:18 d {1 and a~40 d {1 (Table  2 ), which are consistent with what was estimated in vitro  [26]  and also in patients treated with daclatasvir  [14]  and IFN  [9]. The remaining parameters were the four treatment effectiveness parameters E a , E s , c and k and these were estimated using the same method as used for the biphasic model.
Due to the extraordinary scale of the exercise (number of models and variables, spatial and temporal extent), this study is primarily aimed at illustrating the potential for using MOZAIC data for regional-scale evaluation and the capabilities of models to simulate concentration and meteorological fields in the vertical rather than just at the ground. We apply various approaches, metrics, and methods to analyze this complex dataset. Results of the investigation indicate that, while the observed meteorological fields are modelled with some success, modelling CO in and above the boundary layer remains a challenge and modelling ozone also has room for significant improvement. We note, however, that the high sensitivity of models to height, season, location, and metric makes the results rather difficult to interpret and to generalize.
Back Close Full This study demonstrates the existence, usability, and usefulness of observational information in the vertical that can be combined to produce relevant information for model development, evaluation, and improvement. The exploitation of upper air information produced by different sources over a regional domain allows a thorough verification of model fundamentals that is seldom achieved, not because they are assumed to be unimportant but because of the difficulties in finding harmonized measurement data (or finding them collected over long periods). This is the first study of its kind. Although it is not intended to be an in-depth diagnostic analysis, it illustrates the potential for using MOZAIC data for regional-scale evaluation, introduces various approaches, metrics, and methods to analyze this complex dataset, and sets the stage for future work.
Processing of four-dimensional observational data in the context of AQMEII In this study, we make use of the observational data for the year 2006 gathered in the context of the MOZAIC programme (Measurements of Ozone, water vapor, carbon monoxide and nitrogen oxides by Airbus In-service aircraft; http://mozaic. aero. obs-mip. fr/web/) and made available in the ENSEMBLE system  (Galmarini et al. , 2004 (Galmarini et al. , , 2012) )  in the context of the AQMEII inter-comparison and model evaluation activity. In addition to the MOZAIC data, ozonesonde measurements are also used for further comparison in special cases. The MOZAIC project started in 1993 as a joint effort of European scientists, aircraft manufacturers and airlines for better understanding of the natural and human-induced variability of the chemical composition of the atmosphere.
The vast collection of data (wind speed and direction, temperature, relative humidity) and chemical species (CO, NO, NO x , NO y , O 3 ). The sampling rate of data collection is of 4 s, corresponding to approximately 50-100 m in the vertical. The limit of detection is rather low (2 ppbv for ozone) with error of 2 %, thus making the MOZAIC dataset very accurate. Details of MOZAIC data acquisition can be found in  Marenco et al. (1998); Thouret et al. (1998). Permission to access to the MOZAIC dataset for the reference year of 2006 was granted to the AQMEII community, allowing the analysis of over 2000 vertical profiles measured by instrumented commercial aircraft landing and taking-off from 12 selected airports in North America (Portland, Vancouver, Atlanta, Dallas, Charlotte, Boston, Chicago, Montreal, New York, Philadelphia, Toronto, Washington, DC) and three in Europe (Frankfurt, Munich and Vienna).
These profiles contain the following species and meteorological fields: ozone, carbon monoxide (CO), temperature (T ), relative humidity (RH), wind speed (WS) and direction (WD). Modelling groups were requested to deliver data of the same species. Observation and model outputs have been paired in the web shared ENSEMBLE system, as detailed hereafter. The strategy adopted within AQMEII for the comparison with the MOZAIC dataset consisted of plotting all the aircraft trajectories up to the altitude of approximately 13 km at all available airports for 2006 in the two continents. Domains were then identified at each airport location that contained all the trajectories over the year. This was particularly useful in the case, for example, of the Northeast US where trajectories of aircrafts landing in Washington, DC passed over New York airports too.
Participating models The participating models and modelling groups are reported in Table  2. In total, four to five (depending on the variable) modelling groups delivered data to compare against the MOZAIC profiles for NA, whereas outputs from eight to nine modelling groups were available for EU. For NA, four different AQ models were employed: -AURAMS  (Gong et al. , 2006; Smyth et al. , 2009)  -CAMx (ENVIRON, 2010) -CMAQ  (Byun and Schere, 2006)  -DEHM  (Brandt et al. , 2012)  The above AQ models were driven by meteorological inputs generated by the GEM, WRF, COSMO-CLM (CCLM), and MM5 meteorological models. In particular, two sets of CMAQ results are available, which used meteorological inputs from CCLM and WRF, respectively. This pair of results can thus provide some insights into the impact of the meteorological module used on the vertical variability of AQ variables.
Mean vertical profiles We present comparison of seasonal averaged mean profiles of ozone and annual averaged mean profiles of CO, temperature, RH and WS against observational MOZAIC data. Profiles are shown in Fig. 2. The data used are the vertical profiles (ascending and descending) spatially averaged over the areas around the airports. Observations are the symbols in grey. It can be firstly observed how all models show very similar mean profiles of wind (above the PBL) and temperature (from ground to last level), in agreement with the observed ones, although these fields are generated by meteorological models run by different groups. This most likely descends from the influence of nudging technique applied to the input analysis fields. One interesting aspect of the WS profiles is that the positive bias of the MM5-DEHM, that increases with the altitude (all sites).
13 , where the normalised bias of: the BCs with respect to the MOZAIC, and the modelled ozone concentration with respect to MOZAIC are plotted. The regression analysis shows some high correlation coefficients (especially for AURAMS and WRF-CMAQ). When the data of the first levels close to the ground are removed, the correlation coefficients are even higher. This is not entirely surprising as  Schere et al. (2012)  found that the boundary concentration profile specification for ozone was very influential on AQMEII model results far into the interior of the model simulation domain. This was especially true for the winter months and for rural areas away from major emission sources. Systematic underestimates of tropospheric ozone by the global modeling system that was used to derive the boundary concentrations for AQMEII modeling caused the regional-scale models to often underestimate the sharp decreasing gradient taking place in the MOZAIC profile and confirmed by the measurements at ground level receptors.
Summary and conclusions This study is conducted in the framework of the AQMEII activity and presents evaluation of regional scale air quality models. The evaluation is supported by measurements gathered in the MOZAIC campaign. The amount of information we present is unprecedented. Fifteen modelling groups have delivered 3-D data of ozone, CO, wind field, relative humidity with hourly resolution for the full year of 2006 at selected areas around major airports in Europe and North America. All of these data have been paired with observations in the ENSEMBLE system and made ready for analysis. Although this study is not aimed at in-depth diagnostic analysis, it illustrates the potential for using MOZAIC data for regional-scale evaluation, it introduces various approaches, metrics, and methods to analyze this complex dataset, and it sets the stage for future work.
3-D wind fields are applied from the MM5 model, corrected in DEHM to ensure mass conservation. DEHM also includes a module for diagnostically calculating the vertical Discussion Paper | Discussion Paper | Discussion Paper |wind. For ozone, the initial and boundary conditions (lateral and top) are based on ozonesonde measurements, interpolated to global monthly 3-D values with a resolution of 4• × 5• (Logan, 1999). A thorough description of DEHM and model setup for AQMEII is given inBrandt et al. (2012).
Extensive manual annotation of the genomic complement of porcine immune response genes The Immune Response Annotation Group (IRAG) members used Otterlace  [45, 46]  to manually annotate over 1,400 loci in porcine build 9 selected based on their membership in immune response processes or Gene Ontology immune response annotation. The GO term used as an inclusion criterion was "immune system process"; GO:0002376. These structural annotations were transferred over to build 10. 2 and discrepancies were addressed to determine the final annotation results (Table  1 ). Members confirmed automated annotation of 988 known genes through manual annotation of 3,472 transcripts and 1,369 gene models; 1,554 annotated transcripts contained the full-length protein coding sequence. Twenty-six pseudogenes were also identified during the annotation.
Importantly, the cross-species alignment tools in Otterlace allowed annotation of 1,172 transcripts in the pig genome using only mRNA sequence from other species (Table  1 ). Such transcripts without specific porcine sequence support were often made using human cDNAs (and proteins), as there are many more human than pig sequences in the databases. The conservation between human and pig in terms of synteny is three times greater than between human and mouse  [48] , and the pig is more closely-related at the DNA sequence level to humans than either is to the mouse  [16]. Specifically for immune gene families, recent analysis at the cDNA sequence level of pig, mouse and human has shown that the great majority of human genes that were lost through evolution in the mouse were retained in the pig.
The differential expression patterns of these genes support their phylogenetic and functional divergence. Functional annotation of immune genes: finding genes with immune response patterns similar to known immune system genes Co-expression of genes can be used to provide evidence for membership in specific processes, such as immune response, when a substantial proportion of the members of an expression cluster have similar functions. We collected all data reported for the 24K Affymetrix Genechip for experiments with an immune component such as infection of tissues or cells with bacteria, viruses, or stimulation with lipopolysaccharide. Using this targeted set of 188 chips (Additional file 5: Table  S5 ), which included public data as well as several un-published datasets from our groups, we calculated a within-group correlation for each experimental dataset.
Thus cluster 4 clearly is enriched for a large number of genes likely to be involved in the immune response of swine. Importantly, most of these probesets are not already annotated as immune response genes, as only 16% have the GO: 0002376 term annotation. Further, the average RNA level for these 619 probesets across the immune response datasets is shown in Figure  3. The pattern is clearly one of activation upon infection or treatment, as global increases in expression levels are seen in many datasets upon immune stimulation. Thus this correlation analysis provides evidence for the involvement of many new genes in the porcine immune response. A second cluster with enrichment of GO:0002376 is cluster 17 (p < 0. 0001). As with cluster 4, cluster 17 is also enriched for Jenner-Young (J-Y) IR genes as well as genes manually annotated as "innate immune" genes in InnateDB (www.