dataesr/scientific-paragraphs-categorization huggingface dataset:
Linear bias measurements Given our galaxy clustering results, we can now use Eq. 1 to compute the linear bias in each redshift bin for our Euclid-like and WFIRST-like surveys. Each galaxy correlation function is divided by the non-linear dark matter correlation function computed at the same effective redshift. The dark matter correlation functions, which we compute using the CLASS and HALOFIT functionality in the NBodyKit python package and assuming an MXXL cosmology (see Fig. 1 ), are shown with black dashed lines in the panels in the upper row of each grid in Fig. 9  and Fig. 10. The lower row of panels in each grid in Fig. 9  and Fig. 10  show the measured linear bias as a function of separation. As with the correlation functions, the darker lines correspond to the bias estimated using the redshift-space galaxy correlation functions and the fainter lines correspond to the bias estimated using the real-space galaxy correlation functions.
We find that the lightcones are able to independently reproduce the WISP number counts (Fig. 4 ) and the HiZELS luminosity functions (Fig. 6 ), but that different values for AHα are required to reproduce these datasets (Fig. 5 ). The cause of this difference is uncertain, but may be due to cosmic variance between the WISP and HiZELS surveys, differences in the selection functions of the surveys or the WISP number counts probing a restricted luminosity range compared to the HiZELS luminosity functions. We therefore proceed with our bias forecasts considering both a WISP-calibrated version and a HiZELS-calibrated version of each lightcone. To make bias forecasts we first build our Euclid-like and WFIRST-like surveys by using random sampling to apply incompleteness to the appropriate lightcone.
9  and Fig. 10 ). Comparing the correlation functions in real-space and redshift-space we see that the correlation function in redshift-space has a higher amplitude, consistent with theory of redshift-space distortions from  Kaiser (1987). To compute the linear bias we divide the galaxy correlation function in each redshift bin by the non-linear dark matter correlation function, which we compute at the effective redshift of the galaxies in that bin using the NBODYKIT  (Hand et al. 2018 ) python package. We find that for scales r 75 h -1 Mpc the measured linear bias in each redshift bin is consistent with a fixed, scaleindependent value. However, at larger scales the measured bias displays scale-dependent deviations away from a constant. These sinusoidal-like deviations, which are more pronounced in redshiftspace, occur at scales around the BAO scale, which suggests that they could be being caused by distortions of the shape and position of the BAO in the galaxy correlation function relative to the shape and postion predicted by linear theory.
Figure1. Non-linear matter power spectra (top panel) and dark matter twopoint correlation function (bottom panel) for an MXXL cosmology for redshifts in range 0. 8 z 2, as indicated by the inset colour bar. All power spectra and correlation functions were computed using the CLASS and HALOFIT functionality in the NBodyKit python package (Hand et al. 2018 ). In the upper panel a vertical dotted line indicates the wavenumber corresponding to a co-moving distance of 150 h -1 Mpc. In the lower panel the BAO peak is clearly visible at approximately 110 h -1 Mpc.
ACKNOWLEDGEMENTS We thank the anonymous referee for their many helpful comments and suggestions that greatly enhanced the discussion of this work. We additionally thank  Anahita Alvari ,  Iary Davidzon ,  Andreas Faisst ,  Zhongxu Zhai , and the members of the  JPL Darksector group  for various insightful conversations that helped improve this work. AM acknowledges sponsorship of a  NASA   Postdoctoral Program  Fellowship. AM was supported by  JPL , which is run under contract by  California Institute of Technology for NASA. This work was supported by  NASA  ROSES grant  12-EUCLID12-0004  and by  NASA  grant  15-WFIRST15-0008  Cosmology with the  High Latitude Survey WFIRST Science Investigation Team (SIT). This work used the DiRAC@Durham facility managed by the  Institute for Computational Cosmology  on behalf.
Generating Skymaps with CLUMPY v3. 0 For all our calculations, we rely on the public CLUMPY code described in  [4] [5] [6]. It is a flexible code that efficiently emulates the end-product of numerical simulations in terms of γ-ray and neutrino signals for DM annihilation or decay. It allows easy exploration of how results are affected when changing the properties of the DM halos. CLUMPY v2. 0  [5]  was used for this purpose, to estimate the sensitivity of the CTA  [1]  and HAWC  [27]  γ-ray telescopes to galactic DM subhalos. Aside from galactic subhalo studies, CLUMPY has also been used by several teams to model DM annihilation or decay in γ-rays or ν in many targets: dwarf spheroidal galaxies  [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] , the galactic halo  [47] [48] [49] , the Smith HI cloud  [50] , nearby galaxies  [51] , galaxy clusters  [52] [53] [54] , and also for the extragalactic diffuse emission  [55].
Predictions or limits based on galactic halo models which do not account for these constraints should be taken with caution. All our calculations were performed with the public code CLUMPY, and our results illustrate how this code can quickly be used to incorporate and exploit any progress made by numerical simulations and/or semi-analytical calculations. All computations and drawing of random realizations of the discussed models can be repeated at one's own account. Also, the subhalo skymaps and catalogs shown here as illustration for the various models are available upon request.
Acknowledgments: We thank  T. Kelley  and  J. S. Bullock  for providing us, prior to the public release, with the Phat-ELVIS subhalo catalog. We also thank  M. Doro  and  M. A. Sánchez-Conde  for inviting us on this special issue, and for organizing the  Halo Substructure  and  Dark-Matter Searches  workshop held in Madrid in 2018, where discussions prompted this work. Calculations were performed at the  Max Planck Computing & Data Facility at Forschungszentrum Garching. We finally thank the anonymous referees for their reviews and the valuable comments which helped to improve the quality of the manuscript.
Abstract. Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces (e. g. , available big data, developed data assimilation (DA) techniques, and advanced cyber-infrastructure) are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD, v1. 0) into models. EcoPAD (v1. 0) is a web-based software system that automates data transfer and processing from sensor networks to ecological forecasting through data management, model simulation, data assimilation, forecasting, and visualization. It facilitates interactive data-model integration from which the model is recursively improved through updated data while data are systematically refined under the guidance of model.
The system is designed to streamline web request-response, data management, modeling, prediction, and visualization to boost the overall throughput of observational data, promote datamodel communication, inform ecological forecasting, and improve scientific understanding of ecological processes (see the Supplement for detailed functionalities of EcoPAD v1. 0). To realize such data-informed ecological forecasting, the essential components of EcoPAD (v1. 0) include experiments and data, process-based models, data assimilation techniques, and the scientific workflow (Figs. 1 2 3 ). The scientific workflow of EcoPAD (v1. 0) that wraps around ecological models and data assimilation algorithms acts to move datasets in and out of structured and cataloged data collections (metadata catalog), while leaving the logic of the ecological models and data assimilation algorithms untouched (Figs.
Data Data are an important component of EcoPAD (v1. 0) and Eco-PAD (v1. 0) offers systematic data management to digest diverse data streams. The "big data" ecology generates a large volume of very different datasets across various scales  (Mouquet et al. , 2015; Hampton et al. , 2013). These datasets might have high temporal resolutions, such as those from real-time ecological sensors, or the display of spatial information from remote sensing sources and data stored in the geographic information system (GIS). These datasets may also include, but are not limited to, inventory data, laboratory measurements, FLUXNET databases, or data from long-term ecological networks  (Baldocchi et al. , 2001; Johnson et al. , 2010; Robertson et al. , 2012). Such data contain information related to environmental forcing (e.
g. , precipitation, temperature, and radiative forcing), site characteristics (e. g. , soil texture and species composition), and biogeochemical composition. Datasets in EcoPAD (v1. 0) are derived from other research projects in comma-separated value files or other loosely structured data formats. These datasets are first described and stored with appropriate metadata via either manual operation or scheduled automation from sensors. Each project has a separate folder where data are stored. Data are generally separated into two categories. One is used as boundary conditions for modeling and the other category is related to observations that are used for data assimilation. Scheduled sensor data are appended to existing data files with prescribed frequency. Attention is then given to how the particular dataset varies over space (x, y) and time (t).
Data assimilation Data assimilation is growing in importance as process-based ecological models, despite largely simplifying the real systems, need to be complex enough to address sophisticated ecological issues. These ecological issues are composed of an enormous number of biotic and abiotic factors interacting with each other. Data assimilation techniques provide a framework to combine models with data to estimate model parameters  (Shi et al. , 2016) , test alternative ecological hypotheses through different model structures  (Liang et al. , 2015) , assess the information content of datasets  (Weng and Luo, 2011) , quantify uncertainties  (Zhou et al. , 2012; Weng et al. , 2011; Keenan et al. , 2012) , derive emergent ecological relationships  (Bloom et al. , 2016) , identify model errors, and improve ecological predictions  (Luo et al.
Metadata catalog and data management Datasets can be placed and queried in EcoPAD (v1. 0) via a common metadata catalog, which allows for effective management of diverse data streams. Calls for good management of current large and heterogeneous ecological datasets are common  (Vitolo et al. , 2015; Michener and Jones, 2012; Ellison, 2010). Kepler  (Ludascher et al. , 2006)  and the Analytic Web  (Osterweil et al. , 2010)  are two example systems that endeavor to provide efficient data management through the storage of metadata, including clear documentation of data provenance. Similarly to these systems, EcoPAD (v1. 0) takes advantage of modern information technology, especially the metadata catalog, to manage diverse data streams. The Eco-PAD (v1. 0) metadata scheme includes a description of the data product, security, access pattern, and time stamp of last metadata update.
We use MongoDB (https://www. mongodb. com/, last access: January 2019), a NoSQL database technology, to manage heterogeneous datasets to make documentation, query, and storage fast and convenient. Through Mon-goDB, measured datasets can be easily fed into ecological models for various purposes such as to initialize the model, calibrate model parameters, evaluate model structure, and drive model forecasts. For datasets from real-time ecological sensors that are constantly updating, EcoPAD (v1. 0) is set to automatically fetch new data streams with adjustable frequency according to research needs.
0) into a website with a single line of html code. Users will also be able to access data directly through programming environments like R, Python, and MATLAB. Simplicity, ease of use, and interoperability are among the main advantages of this API, which enables web-based modeling. Celery (https://github. com/celery/celery, last access: January 2019) is an asynchronous task or job queue that runs in the background (Fig. 3 ). The task queue (i. e. , Celery) is a mechanism used to distribute work across work units such as threads or machines. Celery communicates through messages, and EcoPAD (v1. 0) takes advantage of the RabbitMQ (https://www. rabbitmq. com/, last access: January 2019) to manage messaging. After the user submits a command, the request or message is passed to Celery via the RESTful API.
Structured result access and visualization EcoPAD (v1. 0) enables structured result storage, access, and visualization to track and analyze data-model fusion practice. Upon the completion of the model task, the model wrapper code calls a post-processing callback function. This callback function allows model-specific data requirements to be added to the model result repository. Each task is associated with a unique task ID and model results are stored within the local repository that can be queried by the unique task ID. The storage and query of model results are realized via the MongoDB and RESTful API (Fig. 3 ). Researchers are authorized to review and download model results and param-eters submitted for each model run through a web-accessible URL (link). The EcoPAD (v1. 0) web page also displays a list of historical tasks (with URL) performed by each user.
SPRUCE project overview EcoPAD (v1. 0) is being applied to the SPRUCE experiment located at the USDA Forest Service Marcell Experimental Forest (MEF; 47 • 30. 476 N, 93 • 27. 162 W) in northern Minnesota  (Kolka et al. , 2011). SPRUCE is an ongoing project that focuses on long-term responses of northern peatland to climate warming and increased atmospheric CO 2 concentration  (Hanson et al. , 2017). At SPRUCE, ecologists measure various aspects of responses of organisms (from microbes to trees) and ecological functions (carbon, nutrient, and water cycles) to a warming climate. One of the key features of the The SPRUCE project generates a large variety of observational datasets that reflect ecosystem dynamics from different scales and are available from the project web page (https://mnspruce.
ornl. gov/, last access: January 2019) and file transfer protocol (FTP) site (ftp://sprucedata. ornl. gov/, last access: January 2019). These datasets come from multiple sources, including half-hourly automated sensor records, species surveys, laboratory measurements, and laser-scanning images. The involvement of both modeling and experimental studies in the SPRUCE project creates the opportunity for data-model communication. Datasets are pulled from SPRUCE archives and stored in the EcoPAD (v1. 0) metadata catalog for running the TECO model and conducting data-model fusion or forecasting. The TECO model has been applied to simulate and forecast carbon dynamics with productions of CO 2 and CH 4 from different carbon pools, soil temperature response, snow depth, and freeze-thaw cycles at the SPRUCE site  (Jiang et al.
Despite the fact that these results are preliminary, as more relevant datasets are under collection with current ongoing warming manipulations and measurements, assimilating observations through EcoPAD (v1. 0) provides a quantitative approach to assess acclimation through time. Melillo et al. (2017)  revealed that the thermal acclimation of soil respiration in Harvard Forest is likely to be phase (time) dependent during their 26-year soil warming experiment. Eco-PAD (v1. 0) provides the possibility to trace the temporal path of acclimation with its streamlined structure and archive capacity. Shi et al. (2015)  assimilated carbon-related measurements in a tallgrass prairie into the TECO model to study acclimation after 9-year warming treatments. They revealed a reduction in the allocation of GPP to shoot, the turnover rates of the shoot and root carbon pools, and an increase in litter and fast carbon turnovers in response to warming treatments.
This point seems not to have been taken into account in other models. of disentangling the contribution from different sources. For example, model intercomparison projects (MIPs), such as TRENDY, focus on uncertainty caused by different model structures with prescribed external forcing  (Sitch et al. , 2008). Keenan et al. (2012)  used data assimilation to constrain parameter uncertainties in projecting Harvard Forest carbon dynamics. Ahlstrom et al. (2012)  forced one particular vegetation model by 18 sets of forcings from climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5), while the parameter or model structure uncertainty is not taken into account. EcoPAD (v1. 0) is designed to provide a thorough picture of uncertainties from multiple sources, especially in carbon cycling studies.
By focusing on multiple instead of one source of uncertainty, ecologists can allocate resources to areas that cause relatively high uncertainty. The attribution of uncertainties in EcoPAD (v1. 0) will rely on an ensemble of ecosystem models, the data assimilation system, and climate forcing with quantified uncertainty. Jiang et al. (2018)  focused specifically on the relative contribution of parameter uncertainty vs. climate forcing uncertainty in forecasting carbon dynamics at the SPRUCE site. By assimilating pretreat-ment measurements (2011-2014) from the SPRUCE experiment,  Jiang et al. (2018)  estimated uncertainties in key parameters that regulate peatland carbon dynamics. Combined with the stochastically generated climate forcing (e. g. , precipitation and temperature),  Jiang et al.
Case 4: improving biophysical estimation for better ecological prediction Carbon cycling studies can also benefit from EcoPAD (v1. 0) through improvements in biophysical estimation. The soil environmental condition is an important regulator of belowground biological activities and also feeds back to aboveground vegetation growth. Biophysical variables, such as soil temperature, soil moisture, ice content, and snow depth, are key predictors of ecosystem dynamics. After constraining the biophysical module by detailed monitoring data from the SPRUCE experiment through the data assimilation component of EcoPAD (v1. 0),  Huang et al. (2017)  forecasted soil thermal dynamics under future conditions and studied the responses of soil temperature to hypothetical air warming. This study emphasized the importance of accurate climate forcing in providing robust thermal forecasts.
3. 4. 5 Case 5: how do the updated model and data contribute to reliable forecasting? Through constantly adjusted model and external forcing according to observations and weekly archived model parameter, model structure, external forcing, and forecasting results, the contribution of model and data updates can be tracked by comparing forecasted vs. realized simulations. For example, Fig. 6  illustrates how updated external forcing (compared to stochastically generated forcing) and shifts in ecosystem state variables shape ecological predictions. "Updated" means the real meteorological forcing monitored from the weather station. We use stochastically generated forcing to represent future meteorological conditions. Future precipitation and air temperature were generated by vector autoregression using a historical dataset  monitored by the weather station.
Photosynthetically active radiation (PAR), relative humidity, and wind speed were randomly sampled from the joint frequency distribution at a given hour each month. Detailed information on weather forcing is available in  Jiang et al. (2018). Similarly as in other EcoPAD-SPRUCE case studies, TECO is trained through data assimilation with observations from 2011-2014 and used to forecast GPP and total soil organic carbon content at the beginning of 2015. For demonstrating purposes, Fig. 6  only shows three series of forecasting results instead of updates from every week. Series 1 (S1) records forecasted GPP and soil carbon with stochastically generated weather forcing from January 2015-December 2024 (Fig. 6a , b, cyan). Series 2 (S2) records simulated GPP and soil carbon with observed (updated) climate forcing from January 2015 to July 2016 and forecasted GPP and soil carbon with stochastically generated forcing from August 2016-December 2024 (Fig.
The necessity of interactive infrastructure to realize ecological forecasting Interactions enable the exchange and extension of information to benefit from collective knowledge. For example, manipulative studies will have a much broader impact if the implications of their results can be extended from the regression between environmental variables and ecosystem response, such as being integrated into an ecosystem model through model-data communication. Such an approach will allow us to gain information about the processes responsible for ecosystem response, constraining models, and making more reliable predictions. Going beyond the common practice of model-data assimilation, in which model updating lags far behind observations, EcoPAD (v1. 0) enables iterative model updating and forecasting by dynamically integrating models with new observations in near-real time.
This near-realtime interactive capacity relies on its scientific workflow that automates data management, model simulation, data simulation, and result visualization. The system design encourages thorough interactions between experimenters and modelers. Forecasting results from SPRUCE were shared among research groups with different backgrounds through the web interface. Expertise from different research groups was integrated to improve a second round of forecasting. Again, thanks to the workflow, new information or adjustments are incorporated into forecasting efficiently, making the forecasting system fully interactive. We also benefit from the interactive EcoPAD (v1. 0) platform to broaden user-model interactions and to broadcast forecasting results. Learning about ecosystem models and data-model fusion techniques may undermine one's productivity and even discourage researchers from learning modeling techniques because of their complexity and long learning curve.
Because EcoPAD (v1. 0) can be accessed from a web browser and does not require any coding from the user's side, the time lag between learning the model structure and obtaining model-based results for one's study is minimal, which opens the door for non-modeler groups to "talk" with models. The online storage of one's results lowers the risk of data loss. The results of each model run can be easily tracked and shared with a unique ID and web address. In addition, the web-based workflow also saves time for experts with automated model running, data assimilation, forecasting, structured result access, and instantaneous graphic outputs, bringing the possibility for a thorough exploration of more essential parts of the system. The simplicity in use of EcoPAD (v1. 0) at the same time may limit access to the code and lower flexibility.
Flexibility for users with higher demands, for example those who want to test alternative data assimilation methods, use a different carbon cycle model, change the number of calibrated parameters, or include observations for other variables, is provided through the GitHub repository (https://github. com/ou-ecolab, last access: January 2019). This GitHub repository contains code and instructions for installing, configuring, and controlling the whole system; users can adapt the workflow to wrap their own model based on their needs. On the one hand, the opensource web-based system broadens the user community. On the other hand, it increases the risk of misuse and misinterpretation. We encourage users to be critical and consult system developers to avoid inappropriate application of the system.