dataesr/scientific-paragraphs-categorization huggingface dataset:
By collecting counts of each species, analysts can better estimate population abundance  (Johnston et al. 2015). eBird also collects information on whether the observer was stationary or traveling, the distance they traveled, the duration of the checklist, and the number of observers. eBird complete checklists are translated into information describing patterns of species occurrence and abundance in space and time using Species Distribution or Niche models (SDMs). These statistical models estimate the distribution or abundance of a species by estimating relationships between the observed patterns of species occurrence, or counts, and data describing the processes that give rise to these observations  (Franklin 2009). To understand ecological processes, each location where eBird data are collected is linked to remotely sensed habitat and elevation data, which are included as explanatory variables in the SDM.
Comparable to modeling ecological processes, similar strategies can be used to account for sampling biases inherent in the observation process in the SDM by using the information that eBird collects about the observation process. First, to limit differences in sampling effort, the data used to train the SDM can be limited to complete checklists with similar levels of sampling effort. In addition, to account for the effects of variable search effort within the selected data, the duration and length of each search are included as explanatory variables in the SDM. To account for variation in detection rates associated with changes in species' behavior throughout each day, the time of day that each search is made is included as an explanatory variable. Finally, to account for the strong differences between observer's ability to detect and identify species, we have calculated the Checklist Calibration Index  (Kelling et al.
2015) , which is derived from the rates at which observers accumulate additional species with increasing effort. We also include the index as an explanatory variable in the SDM. In our experience, these explanatory variables that describe the observation process are some of the most important predictors of variation in the data fit by the SDMs. Including these variables leads to improved predictive performance and provides an inferential basis for separating the ecological and observation processes. This allows researchers to model the spatiotemporal variation in bird distributions throughout a species' annual cycle at continental scales  (Fink et al. 2014 , Fink et al. 2010). Above, we list the principles of good practice for analyzing semistructured citizen science data, such as eBird. In a forthcoming article, we outline the practical steps for analyzing eBird data that follow these principles  (Johnston et al.
The inclusion of relative abundance identifies the core range of the species. The weekly relative abundance estimates show where a species occurs and its relative abundance for every week of the year. The estimates also show regions in which the species does not occur and locations where eBird does not have sufficient data. When combined and analyzed appropriately, eBird data enable next generation species distribution models that provide full life cycle information about birds at relatively fine scales across broad spatial and temporal extents. Recently, estimates of the status and trends of more that 100 species of birds was released on the eBird website (https://ebird. org/science/status-and-trends). The eBird status and trends provide an unparalleled window into the full annual cycle of bird populations in North America.
Maps, charts, and other products explore the range, abundance, habitat, and population trends for each species. Together these metrics and species summaries can be used to contrast regions, seasons, and species, or to inform management decisions. For example, modeling how bird populations change throughout the year has uncovered complex and seasonally varying species-environment relationships  (Zuckerberg et al. 2016) , identified novel aspects of habitat associations that can affect bird populations during migration (La  Sorte et al. 2017) , and identified seasonal resources needed for supporting bird populations during critical stages of their life history  (Johnston et al. 2015 , Reynolds et al. 2017). Overall, eBird collects sufficient data to account for a large proportion of the variation in the observation process, which provides greater confidence in trends, maps, and other ecological metrics that are produced using eBird data.
For example, already more than 60% of all eBird data are being submitted via mobile apps, with this percentage growing. The eBird app automatically gathers most of the observation process data that eBird requires, with little additional effort required from the observer. Nevertheless, increasing rigor and standardization in data collection will need additional coordination and communication from project personnel. In summary, it is the responsibility of the project coordinators to carefully assess the project design and data collection necessary to ensure they are collecting semistructured rather than unstructured data to improve the scientific outcomes of the data collected. Developments in the field of Artificial Intelligence, combined with increasingly efficient and powerful mobile technologies, will vastly improve the quality and quantity data of collected by citizen scientists.
For example, projects such as the Cornell Lab of Ornithology's Merlin Bird ID App and iNaturalist already use powerful Deep Learning algorithms and computer vision techniques to identify images of thousands of organisms to species, helping observers get a species-level identification in the field  (Van Horn et al. 2017). In addition, to encourage a more even distribution of surveys across a region or across habitat gradients, the eBird-based project called Avicaching used behavioral economics to incentivize site selection among the participants. Incentives were used to gather data from locations that were historically undersampled, using discrete choice models and machine learning to account for variable patterns of human behavior  (Xue et al. 2016). The incentives resulted in significant shifts in participant effort to under sampled areas, demonstrating how incentives can be used to collect less-biased data in citizen science programs.
Acknowledgments This work has its origin in a Future Earth supported meeting 6-10  March 2016 at Monte Verita, Switzerland entitled  " Global Biodiversity Assessment and Monitoring-Science, Data and Infrastructure Needs for IPBES and Beyond. " A follow-up meeting was supported by the  Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( FZT 118 ). Additional funding for this work came from the  Wolf Creek Foundation , The  Leon Levy Foundation ,  The Packard Foundation , and the  National Science Foundation  (grants no. ITR-0427914 , no. DBI-0542868 , no. IIS-0612031 , and no. ABI-1356308  to  S. Kelling ; no. IIS-0920869  to  D. Fink ; no. CISE-0832782 , no. ABI-1262600 , and no. ABI-1458527  to  R.
Photonic Electron Background Photonic electrons originate from the Dalitz decay (X → e + e -γ) of π 0 and η mesons, and from the conversion of photons (γ → e + e -) interacting with the beam pipe or the VTX detector itself, where the photons are either direct photons or a hadronic decay product. To model this background, we start with the published cross section of π 0 , η and direct photons in p+p at √ s = 200 GeV  [25] [26] [27] [28]. Single particles are then generated between 0 < p T [GeV/c] < 20 according to the published spectrum. For π 0 and η, accounting for branching ratios, the decay is forced to proceed exclusively through channels involving photons or electrons in the final state. The decay photons and electrons are fed through a geant3 simulation of the PHENIX detector, where the same reconstruction code and track cuts used in data are applied.
C. Normalizing Electron Background DCAT In 2015-the year in which the p+p data was collected-the VTX detector exhibited a time-varying acceptance from a changing number of dead, cold, and hot channels across the surface of each detector layer over time. This precluded the measurement of an electron candidate sample fully corrected for acceptance and efficiency effects. As a result, the simulated electrons in the background cocktail are not corrected for acceptance and efficiency, but simply constructed in such a way that the same reconstruction code and analysis cuts used in data are applied. The cocktail can then be used to calculate the fraction of electrons from each background source relative to the total photonic background. However, to use this information to determine their normalization relative to the total sample of electron candidates, it is necessary to determine the fraction of electron candidates attributable to photonic background.
In this manner, the unfolded yield of D 0 mesons within |y| < 1 can be compared to a measurement by the STAR collaboration  [34]  obtained by fully reconstructing the hadron decays, as shown in Fig. 18. The unfolded D 0 yield is fit with a modified Hagedorn function, with the ratio of data relative to the fit being shown in the bottom panel. Good agreement with results published by STAR is observed within uncertainties. The model dependence of the unfolded charm and bottom cross sections can be reduced by applying the decay model-that is, multiplying the hadron cross sections by the decay matrices-to obtain the refolded cross section of heavy-flavor-decay electrons in the PHENIX central arm acceptance. The result can be normalized to obtain the fully invariant differential cross sections shown in Fig.
The measured bottom electron frac- tion is observed to be consistent with the FONLL calculation within uncertainties. In particular, good agreement with the central FONLL value is seen below p T ≈ 3 GeV/c, with the measured fraction rising slightly above that at higher p T. Figure  21  shows a comparison of the unfolded bottom electron fraction with earlier measurements made by the STAR  [12]  and PHENIX  [36]  collaborations using electron-hadron and electron-D correlations. It is apparent that the size of the dataset, combined with the unfolding method used in the present analysis provides a result with smaller total uncertainty and significantly extended kinematic reach at low p T. Furthermore, the unfolded result provides a more direct determination Fraction of bottom electrons obtained with the unfolding procedure (red), compared to previous measurements by PHENIX  [36]  and STAR  [12]  using electron-hadron correlations.
VI. SUMMARY We have reported on a new measurement of the differential-invariant production cross section of separated-heavy-flavor electrons in p+p collisions at √ s = 200 GeV, at midrapidity |η| < 0. 35 and within 1. 0 < p (e) T < 9. 0 GeV/c. The measurement proceeds via an unfolding analysis where the yield of open-heavyflavor hadrons is inferred from the inclusive-heavy-flavor electron spectrum, and the electron DCA T distribution measured with the PHENIX silicon-vertex detector. The individual yields of charm and bottom electrons, as well as the bottom electron fraction, are found to be consistent with FONLL calculations. This measurement will provide a precision baseline for future-heavy-flavorseparation analyses. In particular, forthcoming PHENIX results using a high-statistics Au+Au dataset promise to reduce current uncertainties and shed light on the centrality dependence of charm and bottom suppression.
We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2. 2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.
, 2011) , as well as to allow estimates to be informed by other types of data  (Jombart et al. , 2014; Campbell et al. , 2019). Because of the importance of tracking temporal changes in epidemiological parameters, software implementing the framework of  Wallinga and Teunis (2004)  was developed to allow such analyses to be performed  (Obadia et al. , 2012). Other methods to estimate reproduction numbers at the start of an epidemic are also reviewed in  Obadia et al. (2012)  and implemented in the same R software package R0. Recognising that estimates of the instantaneous reproduction number may provide a superior real-time picture of an outbreak as it is unfurling,  Cori et al. (2013)  subsequently developed a method and software (the EpiEstim R package) for estimating the instantaneous reproduction number using branching processes.
Rather than relying on previous estimates of the serial interval, our method integrates data on known pairs of index and secondary cases from which the serial interval is directly estimated, with corresponding uncertainty in the serial interval fully accounted for. Our method also allows incorporation of available information on imported (as opposed to locally infected) cases. We use data from the outbreaks of H1N1 influenza in the USA in 2009 and Ebola virus disease in West Africa from 2013 to 2016 to show how directly including the latest serial interval observations can improve the precision and accuracy of estimates of the time-dependent reproduction number during an outbreak. We use data on MERS cases in Saudi Arabia from 2014 to 2015 to illustrate the importance of accounting for imported cases appropriately when quantifying transmissibility.
Our approach is implemented in a new version (2. 2) of the R package EpiEstim (doi: 10. 5281/zenodo. 3333654), as well as an online interactive user-friendly interface (EpiEstim App -accessible at https://shiny. dide. imperial. ac. uk/ epiestim -doi: 10. 5281/zenodo. 3275999) for users that are not familiar with R statistical software.
Estimating the serial interval distribution The distribution of serial intervals can be estimated during an ongoing outbreak using interval-censored line list data -namely lower and upper bounds on timings of symptom onset in index and secondary cases  (Cowling et al. , 2009; Lessler et al. , 2009)  (see Fig. 1a  for an example of such a dataset). Serial interval data of this form are often collected during outbreaks, particularly in household studies from which chains of transmission can be reconstructed  (Fine, 2003; Cauchemez et al. , 2009; Cowling et al. , 2009). Historical Ebola outbreaks provide a number of examples of this. For example, in the Ebola virus disease outbreak in the Democratic Republic of the Congo in 1995, such data were obtained from sources including hospital records and interviews with members of households with cases of Ebola  (Dowell et al.
If there is a single serial interval distribution w s (s = 1,2,…), representing the probability of a secondary case arising a time period s after the primary case, each incident case (either local or imported) that appeared at a previous timestep t-s contributes to the current infectiousness at a relative level given by w s. Therefore conditional on w s , t can be computed as = + = = = w I I w I w ( ) ( ) t s s t t s t s s s t t s s 1 local imported 1. Fig. 1. A schematic illustrating how disease incidence time series and data on the serial interval can be combined to generate estimates of the reproduction number. Step 1. The serial interval distribution is estimated from interval-censored data on timings of symptom onset in index and secondary cases. Step 2. Estimates of the serial interval distribution are combined with disease incidence data to estimate the time-dependent reproduction number, R t.
In all our analyses here, we choose a and b so that the prior for R t has mean and standard deviation equal to 5, as in  Cori et al. (2013). The choice of a large standard deviation ensures that the prior is relatively uninformative, and the choice of a high mean value for the prior is conservative; i. e. if posterior estimates of R t are below one, indicating that the epidemic is estimated to be under control, then we can be sure that this does not result from the choice of prior but is a direct result of the data. To obtain a sample from the full posterior distribution for R t given both the incidence and the serial interval data, we consider each possible serial interval distribution = … w i n ( 1, ) s i ( ) obtained in the previous section when the serial interval was estimated. For each i, we draw a sample of size m from the gamma posterior distribution of R t , given the incidence data and conditional on the serial interval distribution w s i ( ).
Liberia (2014) -Fig. 4  We also analysed data from the West African 2013-2016 Ebola outbreak. We considered the daily incidence of confirmed and probable cases in Liberia between 28 th May and 31 st July 2014, computed from the World Health Organization line-list data as described by the International Ebola Response Team (International Ebola Response Team, 2016) and shown in Fig. 4a. In this time interval, 418 symptomatic confirmed and probable cases were reported. There were 16 confirmed and probable cases reported before this time, but these occurred sporadically and hence we conducted our analysis using data from 28 th May 2014 onwards.
Ebola virus disease in Line-list serial interval data were available from the World Health Organization (International Ebola Response Team, 2016). Infected individuals were asked who their potential infectors might have been. Up to 31 st July 2014, nine such cases ('early serial interval data') were available for which information on exposure to a confirmed, probable or suspected case could be retrieved in this way. Data from 295 further pairs of cases up until 4 th December 2014 were also available and used in our analyses ('all serial interval data').
MERS in Saudi Arabia (2014-2015) -Fig. 5 A dataset consisting of the daily numbers of laboratory confirmed human cases of MERS in Saudi Arabia between 11 th August 2014 and the 18 th December 2015 was extracted from the EMPRES-I system from FAO (Global Animal Disease Information System -Food and Agriculture Organization of the United Nations, 2017). The dataset indicates which cases were in humans who have regular (potentially infective) contacts with animals, particularly camels. Since the dromedary camel is considered as a reservoir species of the MERS-coronavirus  (Haagmans et al. , 2014) , we interpreted reported regular contact with animals as an indication of infection from the reservoir. This allowed us to distinguish between cases arising from human-human transmission, for example transmission in households or hospitals  (Al-Tawfiq and Perl, 2015) , and human cases derived directly from the animal reservoir.
To illustrate this principle, we estimated the changes in the reproduction number for an outbreak of H1N1 influenza in a school in Pennsylvania in 2009  (Cauchemez et al. , 2011; Donnelly et al. , 2011; Cori et al. , 2013). We used serial interval data collected in a household study undertaken early in the 2009 influenza pandemic in San Antonio, Texas  (Morgan et al. , 2010). We estimated the reproduction number using two subsets of these serial interval data: first, only the data that were available early in the study (for which the primary cases occurred between 17 th -24 th April 2009), and; second, all the data from the study (17 th April-8 th May 2009). Results are shown in Fig. 3. The mean R t estimates using only the early serial interval data were mostly greater than those using all serial interval data.
, 2014; Thompson et al. , 2018). We also analysed data from the West African 2013-2016 Ebola outbreak (see Data section in Methods). The incidence data are shown in Fig. 4a. We computed three estimates of the time-dependent reproduction number using three different assumptions on the serial interval: (i) using a single distribution for the serial interval (yellow line in Fig. 4b ); (ii) using the full posterior distribution of serial intervals estimated from the nine pairs of cases observed up to 31 st July 2014 (blue lines in Fig. 4b  and d ); and (iii) using the full posterior distribution of serial intervals estimated from all 304 pairs of cases observed up to 4 th December 2014 (red lines in Fig. 4d ). In all analyses of the Ebola data, we used an offset gamma serial interval distribution.
Imported cases We used incidence data of MERS cases in Saudi Arabia from 2014 to 2015 (Food and Agriculture Organization of the United Nations, 2017) -shown in Fig. 5a  -to estimate the reproduction number throughout that outbreak. Data were available describing some cases as being likely   b ) Estimates of R t obtained from the incidence data shown in (a), assuming an offset Gamma distribution with mean 6. 8 days and standard deviation 4. 1 days for the serial interval  (Cauchemez et al. , 2016). Solid lines show the mean estimates and the shaded areas show the 95% credible intervals. Results shown in black ignore the information on proximity to the animal reservoir and assume all infections arose from other human cases in the dataset. Results shown in red account for the cases known to be infected from the animal reservoir.
Our approach builds on a well-established method  (Cori et al. , 2013)  and addresses two important limitations of the approach as proposed in that study. The first important feature of our framework is that data on pairs of infector/infected individuals can be included in the estimation procedure, so that the serial interval distribution and the time-dependent reproduction number can be estimated jointly from the latest available data. This leads to more precise estimates of transmissibility, as well as accurate quantification of the uncertainty associated with these estimates. Second, our method allows datasets that distinguish between locally transmitted and imported cases to be analysed appropriately. We describe these limitations of the previous method in more detail below. We have shown that these key features lead to improved inference of pathogen transmissibility, with illustrations using datasets from epidemics of H1N1 influenza (Figs.
4c ). Moreover, ignoring recent data on the serial interval can dramatically impact estimates of the reproduction number and the uncertainty associated with those estimates  (Figs. 3d, 4e). This is of practical importance -as an example, a number of studies conducted during and after the 2013-16 West African Ebola outbreak (e. g. Wiratsudakul et al. , 2016; Bakker and Wallinga, 2016; Dalziel et al. , 2018)  used the same single serial interval estimate obtained near the beginning of the outbreak (  WHO Ebola Response Team, 2014). Our results suggest that using the latest available data on pairs of index and secondary cases, and fully accounting for the corresponding uncertainty in the serial interval estimates, may lead to very different, but more robust estimates of the reproduction number.
g. camels for MERS and nonhuman primates for yellow fever) can be common. Continued importation of cases into a local population from other geographical locations can also occur. For example, a number of cases of H1N1 influenza in New Zealand in 2009 were known imports from other locations  (Paine et al. , 2010). Failing to properly account for such non-locally transmitted cases can lead to overestimating the reproduction number, as we illustrated in our application to data on MERS in Saudi Arabia from 2014 to 2015 (Fig. 5b, c ). Epidemiological studies often collect data on exposure routes for each case  (Cowling et al. , 2008 (Cowling et al. , , 2009;; Forsberg White et al. , 2009; Paine et al. , 2010; Cori et al. , 2017) , and this information on the local or non-local source of incident cases should be included, when available, in estimates of pathogen transmissibility.