dataesr/scientific-paragraphs-categorization huggingface dataset:
Synthetic data-sets In order to test the model of the Ly correlation functions, we use the set of one hundred mock data sets introduced in du Mas des  Bourboux et al. (2020). These mocks were produced for the BAO analysis of the Ly eBOSS DR16 data set. Each mock is based on a Gaussian random field generated with the CoLoRe 2  package  (Ramírez-Pérez et al. 2022). The Gaussian field is transformed into a log-normal density field, which is Poisson sampled based on an input number density and bias in order to obtain a set of quasar sources. CoLoRe then computes line-of-sight skewers by interpolating the initial Gaussian field and the radial velocity field 3  from each quasar to the centre of the box. The skewers generated by CoLoRe require significant postprocessing in order to turn them into realistic simulations of the Ly forest.
This is performed by the LyaCoLoRe 4  package  (Farr et al. 2020). In order to create the large boxes needed to simulate an all-sky light-cone to  = 3. 7 (∼ 10 ℎ -1 Gpc in this case), the CoLoRe grid is limited by memory and computational constraints to a resolution of ∼ 2. 4 ℎ -1 Mpc (for the 4906 3 grid used here). However, one of the contributions to the covariance of 3D correlations (and consequently of BAO uncertainties) is related to the amount of small-scale fluctuations in the Ly forest  (McDonald & Eisenstein 2007) , often characterized by the one-dimensional power spectrum, or  1D. In order to simulate spectra with a realistic  1D , LyaCoLoRe adds an extra 1D Gaussian field to each line-of-sight. After that, the field undergoes a log-normal transformation, and the output is used to compute the optical depth field () using the fluctuating Gunn-Peterson approximation (FGPA;  Bi & Davidsen 1997; Croft et al.
2020). Random redshift errors are drawn from a Gaussian distribution with a standard deviation   = 400 km s -1. The specsim package  (Kirkby et al. 2016 ) is then used to simulate the eBOSS spectral resolution, the pixelization of the spectra, and instrumental noise (du Mas des  Bourboux et al. 2020). While these synthetic data sets were created for BAO studies, in this article we use them to study full-shape analyses. As described above, the major contaminants that are known to affect Ly forest correlations are being modelled in these mocks. This will allow us to test the performance of our model, and how full-shape analyses are affected by these contaminants. However, one of the main concerns when attempting to use the full-shape of correlation functions for cosmology is the ability to accurately model the non-linear effects on small scales.
All the following steps in the analysis are done with both  p fid () and  s fid (), in parallel. We use the Vega package to perform this analysis. 9 he Ly auto and cross power spectra are given by: 𝑃 auto (𝑘, 𝜇 𝑘 , 𝑧) = 𝑏 ′2 Ly𝛼 (1 + 𝛽 ′ Ly𝛼 𝜇 2 𝑘 ) 2 𝐺 (𝑘, 𝜇 𝑘 )𝐹 sm (𝑘, 𝜇 𝑘 )𝑃 fid (𝑘), (5) 𝑃 cross (𝑘, 𝜇 𝑘 , 𝑧) = 𝑏 ′ Ly𝛼 (1 + 𝛽 ′ Ly𝛼 𝜇 2 𝑘 ) 𝐺 (𝑘, 𝜇 𝑘 )𝐹 sm (𝑘, 𝜇 𝑘 ) × 𝑏 QSO (1 + 𝛽 QSO 𝜇 2 𝑘 ) 𝐹 NL (𝑘, 𝜇 𝑘 )𝑃 fid (𝑘), (6) where  ′ Ly and  ′ Ly are the effective Ly bias and RSD parameter, and  QSO and  QSO are the quasar linear bias and RSD parameter. The Ly absorption is given by a combination of contributions from the diffuse IGM and from high column density (HCD) systems. Both trace the underlying density field, but HCDs correspond to regions with significantly higher clustering. Here, we define HCDs as systems with neutral hydrogen column density above 10 17.
RESULTS A large part of the validation of our analysis will focus on studying full-shape fits using a stack of the Ly forest correlation functions from the eBOSS mocks. Fitting the stack of the mock correlations is much less computationally expensive compared to separately fitting all one hundred mock correlations. Therefore, this method is advantageous when we want to test the performance of our model or study different versions of the analysis. This is because it allows us to study the trends necessary without as much of an emphasis on the effect of noise. Our main goal in this section is to test if the current modelling approach for the Ly forest correlation functions is appropriate for full-shape analyses of the eBOSS data. In order to achieve this, we want to gain an understanding of the relation between the statistical precision we expect using the eBOSS data and the systematic bias.
We use a Gaussian likelihood, and compute posterior distributions using the Vega package for modelling and the PolyChord 13  package for sampling  (Handley et al. 2015a,b). When running PolyChord, we initialize a number of live points equal to 25 times the number of parameters, and set the length of the slice sampling chains to be of order of the number of parameters. In Figure  3 , we show the results for   ,   , and   from the joint fit of the two auto-correlations alone, the two-cross correlations alone, and the combination of all four correlations, using the split AP analysis. These fits use a minimum separation  >  min = 25 ℎ -1 Mpc following the analysis in Section 3. 2. We find good agreement between the scale parameters measured from the auto-and cross-correlations. We use the same cosmological model that was used to construct the mocks to also compute comoving coordinates and the power spectrum template.
2017 ) which removes the affected modes in both our data and our models. These distortions are limited to large scales in the power spectrum (low values of  ∥ ), but affect the correlation function on all scales  (Blomqvist et al. 2015; Bautista et al. 2017). Given the comparison in Figure  1  between the stacked correlations and the best fit model, combined with the fact that we can recover unbiased values of  s and   (Figure  4 ), we conclude that our modelling of this distortion is accurate enough for current datasets. While large high column density (HCD) absorbers are masked in the input spectra, those with a typical width smaller than ∼ 14 ℎ -1 Mpc remain (de Sainte  Agathe et al. 2019; du Mas des Bourboux et al. 2020 ). The current model of this effect (Equations 7 and 8) follows the description introduced by Font-Ribera & Miralda-Escudé (2012), combined with an exponential shape for the  HCD ( | | ) function (based on the analysis by  Rogers et al.
We show histograms of the best fit values of the scale parameters 15 Running the sampler for a joint analysis of all four Ly correlations with all the contaminants requires ∼ 2 -3 × 10 3 CPU hours, which leads to a rough estimate of ∼ 2 -3 × 10 5 CPU hours to analyse all 100 mocks. 16 We use the iminuit package  (Dembinski et al. 2021)  for minimization. The top plots show fits of  p (left) and  s (right). These show that we are able to obtain unbiased measurement of  from the full shape of the Ly forest correlations, which is confirmed by the bottom right plot, showing results for. The bottom left plot shows results from fits of the BAO isotropic scale parameter  p. The mean of the population in this case is slightly higher (by ∼ 0. 25) than the expected value. in Figure  7. The top plots show results for  p and  s , both of which are consistent with the expected value of unity.
Therefore, when analysing real data, we can benchmark our BAO result using the results of the eBOSS BAO analysis. In Section 3. 3, we have discussed the impact of the major contaminants that are modelled in the mock data and shown the importance of modelling them accurately. However, we have only touched on the effects that we model and observe in the mock data. While these represent the most important known contaminants for the Ly forest 3D correlations (which is why they are modelled in the mock data), they are not the only possible sources of systematic uncertainty. Here we wish to discuss some other possible sources of contamination, how they have been treated in the past, and also to give recommendations on which of them warrant attention when performing this analysis on real data. The mock data used in this work is based on a Gaussian density field with quasars randomly sampled using its log-normal transformation.
2022) , and the small scale non-linear effects in the crosscorrelation. The other effects discussed here have been studied before, and modelled at some level in past Ly BAO analysis. They were not prioritized by the eBOSS collaboration when creating mocks because they were not found to play an important role in past BAO analyses. However, this may not be true for full-shape analyses. Therefore, an important part of the first full-shape analysis on real data will be to determine if the current models for these secondary effects have an impact on measurements of  s. If they are found to have an impact, it will motivate the development of more accurate mocks in the future.
SUMMARY The Lyman- (Ly) forest provides one of the best tracers of large scale structure (LSS) at high redshifts (2 <  < 4). However, 3D correlations of the forest have so far only been used to measure the baryon acoustic oscillations (BAO) scale. Recently,  Cuceu et al. (2021)  showed that a measurement of the Alcock-Paczyński (AP) effect from the full shapes of Ly forest correlation functions can lead to significant improvements in cosmological constraints compared to BAO-only measurements. In this work, we use synthetic data of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) in order to test such an analysis. We model the Ly auto-correlation, and its cross-correlation with quasars, using a similar approach to that used in past Ly BAO analyses  (Kirkby et al. 2013; Bautista et al.
Our results here reinforce the earlier conclusions. The means of these distributions are consistent with the values expected from the mocks,  = 1. This again indicates that we are able to obtain unbiased measurements of , using the full shapes of the correlations. Finally, in Section 4 we discuss the implications of our results and give recommendations for how such an analysis should be performed on real data from eBOSS. As we have shown, the most important contaminants are well modelled and do not have a significant impact on  measurements for  min ≥ 25 ℎ -1 Mpc. Therefore, we focus on discussing the effects that are not modelled in the synthetic data, and what tests can be done on the real data in order to gauge their impact. Most of these are quite well understood, have already been modelled and tested in past Ly BAO analyses, and were not found to have a significant impact.
ACKNOWLEDGEMENTS AC and PM acknowledge support from the  United States Department of Energy, Office of High Energy Physics  under Award Number  DE-SC-0011726. AFR acknowledges support through the  program Ramon y Cajal  ( RYC-2018-025210 ) of the  Spanish Ministry of Science and Innovation  and from the  European Union 's  Horizon Europe research and innovation programme (COSMO-LYA , grant agreement  101044612 ). IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. BJ acknowledges support by  STFC  Consolidated Grant  ST/V000780/1. SN acknowledges support from an  STFC   Ernest Rutherford Fellowship , grant reference  ST/T005009/2. We acknowledge the use of the GetDist (Lewis 2019) and numpy (Harris et al. 2020) packages. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising.
NEON database and water-quality sensors The National Ecological Observatory Network (NEON) database provides open data from sites across the United States of America (USA). All NEON sites are equipped with high-frequency sensors and follow standardised configuration, calibration and preventive maintenance procedures  (Vance et al. , 2019; Willingham et al. , 2019) , with in-situ measurements and sample analyses following protocols outlined in  Cawley (2016). Nitrate is measured in µM using a 10 mm path length SUNA V2 UV light spectrum sensor. The SUNA V2 collects data reported as a mean value from 20 measurements made during a sampling burst every 15 minutes (Table  1 ). Other sensors collect specific conductance (µS/cm), dissolved oxygen (mg/L), temperature ( • C) and turbidity (Formazin Nephelometric Units, FNU) data as one-minute instantaneous measurements.
Study sites and time-series data We extracted time-series of nitrate (National Ecological Observatory Network, 2021b) and other surface water-quality variables (National Ecological Observatory Network, 2021d,c,a) from three different sites within the NEON database on 29 January 2021 (see Table  1 ): the Arikaree River in Colorado, Caribou-Poker Creeks Research Watershed in Alaska, and Lewis Run in Virginia, which we will refer to as Arikaree, Caribou and Lewis Run, respectively throughout (Table  2 ). Although nitrate measurements were collected more frequently than other water-quality variables (see Table  1 ), all sensors took measurements each time nitrate was sampled. Water-quality measurements from other time points were therefore discarded so that we only used data measured every 15 minutes (i.
e. at each nitrate-measurement time point) in the analyses. Prior to analyses, we also removed data labelled as anomalous (e. g. due to known sensor calibration problems) during the rigorous NEON quality assurance and quality control procedure  (Cawley, 2018). This, along with periods of missing data resulting from flow intermittence or sensors being temporarily out of service, meant that the time series of data from each site differed. For example, the river at Caribou freezes from approximately October to May each year and NEON removes most sensors from the site to prevent damage or loss. Despite these gaps in the data, at least 50% of the time series we examined from any one site overlapped with that of the other sites. Table  2  provides details about the selected data time series for each site, along with the actual time-period that could be used for modelling due to missing data.
Statistical analyses NEON publishes many environmental data products. The "water-quality" data product (National Ecological Observatory Network, 2021d) includes high-frequency pH, dissolved oxygen, oxygen saturation, turbidity, specific conductance, conductivity, chlorophyll-a, and fluorescent dissolved organic matter (fDOM) data streams. The "temperature (PRT) in surface water" product (National Ecological Observatory Network, 2021c) contains highaccuracy temperature data and the "surface water elevation" product (National Ecological Observatory Network, 2021a) includes elevation data derived from pressure transducers and site-surveyed elevations. Among the water-quality variables, many exhibit strong correlation with each other. We investigated multicollinearity in order to select only those variables that were independent or weakly correlated with each other.
To avoid over-fitting of the GAM model, the maximal value of degree of freedom of the smooth terms were fixed at 6. 2. Step two: An autoregressive-moving-average model was fitted to the GAM residuals to take into account the structural dependence of the time series data (GAMM) The best ARMA regression on the GAM residuals were identified, based on the Akaike Information Criterion, to account for temporal autocorrelation in the time series data. We then assessed the statistical significance and importance of each covariate in the best models to better understand their effects on nitrate. Variable importance can be estimated easily with linear models using the partial R 2 (i. e. the proportion of variation explained by a covariate in a model), but this approach cannot be used in a GAMM. Therefore, we compared the deviance explained by the best GAMM and the same GAMM with each water-quality covariate iteratively removed.
The smaller the aAIC, the better the model performance. All analyses were undertaken in R statistical software (R Core Team 2020) using the car (Fox et al. , 2020), gam (Hastie, 2020), mgcv  (Wood, 2017), and forecast (Hyndman et al. , 2021)  packages. The R script used to implement the analyses is provided in supplementary materials and in the GitHub repository: https://github. com/Claire-K/nitrate links/ blob/main/script publi arik. R.
Diel fluctuations in water-quality occurred at all three sites despite differences in the distributions of water-quality data among them (top plots of Figure  2 ). Data from NEON are given in GTM, Arikaree is GTM-7, Caribou is GTM-8 and Lewis Run is GTM-5. Local time at the beginning at the three top plots are 3PM at Arikaree, 2PM at Caribou, and 5 PM at Lewis Run. At Caribou, nitrate, dissolved oxygen, specific conductance and turbidity increased while temperature decreased during the afternoon and the night. At Arikaree, the diel patterns exhibited comparatively more variation than at Caribou, and both nitrate and dissolved oxygen fluctuated in the opposite direction (i. e. decreasing at night and increasing during the day). Lewis Run exhibited the same diel patterns as Caribou and Arikaree, with a clear alternation between day and night in turbidity.
The use of GAMMs with big data has nevertheless raised computational challenges. The first challenge was the presence of missing data and technical anomalies in the time series. Within the two-year period of data analysed for this study, there were extended periods of such data that could not be incorporated into the models, which constrained the lengths of the time series modelled as a result (see supplementary materials). A second challenge was building the GAMMs themselves. The most commonly used function in the R mgcv package to fit GAMMs is 'gamm', which enables an ARMA process to be fit to GAM residuals. However, as explained in the gammmgcv vignette (gammmgcv: Generalized Additive Mixed Models, n. d. ), 'gamm' is typically much slower to run than the 'gam' function, and the amount of memory required by R to run 'gamm' with large data sets may need to be increased substantially.
, 2019). The findings of this study are highly relevant for scientists and managers responsible for in-situ monitoring in rivers. As mentioned above, gaps in in-situ sensor data are common and the methods demonstrated here can be used to impute missing data and provide a more holistic description of nitrate dynamics. This is particularly important when financial resources are limited and decisions must be made about which sensors to buy and which water-quality variables to measure. In addition, this work provides a basis for future studies focused on the prediction of other critically important, water-quality variables that cannot be measured using in-situ sensors or when the sensors themselves are cost prohibitive. and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.
Acknowledgments Funding was provided by the  Energy Environment Solutions  ( E2S-UPPA ) consortium thought an international research chair. This study was part of a project funded by an  Australian Research Council (ARC)   Linkage grant  ( LP180101151 ) "Revolutionising high resolution water-quality monitoring in the information age", and we extend our thanks to all the team members involved in the broader project. Data is available through ( National Ecological Observatory Network , 2021b) (nitrate concentration) and ( National Ecological Observatory Network , 2021d,c,a) (other surface water-quality variables). The  National Ecological Observatory Network  is a program sponsored by the  National Science Foundation.
Introduction To accelerate plant research and manage costs, model species first emerged as a good strategy for studying plant development and stress response, thus providing the research community with data and knowledge. Databases such as TAIR for Arabidopsis thaliana  (Berardini et al. , 2015) , MTGD for Medicago truncatula Gaertn. (Krishnakumar et al. , 2015) , miyakogusa-jp for Lotus japonicus  (Sato et al. , 2008)  or RAP-DB for Oryza sativa  (Ohyanagi et al. , 2006)  were created to centralize, organize and curate the information on model species while providing tools for their analysis. Meanwhile, researchers working on other species have been inferring information from closely-related model plants using orthology and synteny. In fact, orthologs, i. e. genes descending from a common ancestral gene by a speciation event, are likely to have similar and conserved functions  (Linard et al.
These new resources, along with associated annotations, are often being hosted on dedicated websites and/or made available in repositories of well-known databases such as NCBI  (Sayers et al. , 2022) , Ensembl Plants  (Yates et al. , 2022) ,  Gramene (Tello-Ruiz et al. , 2021)  or Phytozome  (Goodstein et al. , 2012). The release of the genome sequences is significantly boosting the production of genetic data to inform on the control of phenotypic traits by genes and the production of -omic data (mostly represented by genomics, transcriptomics, proteomics and metabolomics) to characterize and quantify the different molecules from a biological entity. However, efforts are still uneven across the broad spectrum of species since conducting experiments spanning a wide range of genotypes, tissues and conditions to generate solid data can be very informative, but also expensive and hard to achieve.
Also, some quantitative trait loci (QTL) controlling quantitative traits still display low resolution, either due to low marker density or to low recombination rate in the respective genomic regions, which can result in large number of genes within the confidence intervals and long lists of candidate genes. Comparing QTL positions across species can help pinpointing orthologous ones and thus refining the intervals of those with low resolution. Such comparative translational research has also the potential to transfer functional information from one species to another or to a group of species. Databases are powerful tools to leverage already produced datasets, not only as a mean of storage but also of intelligent exploitation. For example, the Comparative Genomics (CoGe) platform currently allows for the comparison of datasets from a wide range of organisms, with nearly 58,000 genomes available  (Lyons and Freeling, 2008).
Using sequence homology and synteny, researchers can identify structural and nucleotide variations for their species of interest. Researchers can also use the LoadExp+ extension to import experimental data in various common formats, such as VCF for polymorphism or FASTQ for RNA-seq, process them, and display the results as tracks in the genome browsers  (Grover et al. , 2017). Nevertheless, the data are predominantly stored using relational database management system (RDBMS), distributed in category-specific tables. One problem than can arise with RDBMS is that connecting tables containing large datasets during querying requires several joining operations, which are expensive in terms of time and computational resources  (Vicknair et al. , 2010). The intuitive idea of structuring intertwined data into a graph was propelled by the World Wide Consortium for semantic web through the Resource Description Framework (RDF),  (W3C, 1994).
In order to obtain a logical model in RDF, each piece of data is sliced into atomic statements stored as triples, i. e. , (1) the subject of the resource to describe, (2) a property assigned to the resource, termed predicate and (3) the object, either a description or another resource. The subject and the object are nodes in the graph, while the predicate is an edge connecting the two nodes  (Abuoda et al. , 2022). The directional decomposition of information allows the use of ontologies that organize knowledge and greatly improve data sharing in scientific communities  (Stevens et al. , 2020). However, databases using this format, called triplestores or RDF stores, are characterized by an atomic granularity of nodes which can make database modeling tedious. In addition, deep traversal of the graph requires self-joining of all traversed triples which can make the cost of traversing edges logarithmic  (Donkers et al.