dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene ontology terms (Biological Process, Molecular Function, and Cellular Component) associated with each gene model were derived from significant (E < 0. 001) BLASTN hits to the UniProtKB Swiss-Prot database. Detecting signatures of selection in wild Baltic Sea populations. The method of Berg and Coop 59 was used to test for signatures of polygenic selection on experiment-selected SNPs in eight wild populations from the Baltic Sea (Fig. 3a ; Supplementary Data 3). Using this method, a "polygenic score" for freshwater survival was calculated for each population by taking the sum of each experimentselected SNP's frequency multiplied by its effect size. Estimated selection coefficients from the laboratory experiment were used as proxies for effect size for freshwater survival. The Q X statistic, which measures the variance in polygenic scores considering the neutral population history, was calculated to test whether experiment-selected SNPs exhibited signatures of selection.
We considered the f 4 test significant if all three of the possible fourpopulation trees were rejected with a |Z-score| > 2. (v. 0. 0. 1 127 ). Data used in this study from publicly available databases include the International Council for the Exploration of the Sea (ICES) database (https://ocean. ices. dk/ Helcom/Helcom. aspx?Mode=1), NCBI RefSeq (https://www. ncbi. nlm. nih. gov/refseq/), the Pfam database (https://pfam. xfam. org/), and the UniProtKB Swiss-Prot database (https://www. uniprot. org/uniprot/?query=reviewed:yes).
Acknowledgements This work was supported by the  National Science Foundation   OCE-1658517 ,  NSF   DEB-2055356 , and  French National Research Agency   ANR-19-MPGA-0004  to C. E. L. , the  Michael Guyer Postdoctoral Fellowship  and the  NIH/NHGRI Genome Sciences Postdoctoral Traineeship  ( NIH-5T32HG002760 ) to D. B. S. , and  UW-Madison Department of   Integrative Biology Graduate Summer Research Awards  to J. A. D. and N. W. A. , and  UW-Madison Graduate Recruitment Fellowship  to J. A. D. The authors would like to thank the scientists and crew members of the  F. S. Alkor  and the  R. V. Aranda , especially  Jan Dierking ,  Thorsten Reusch , and  Pekka Kotilainen , for their help collecting E. affinis from the Baltic Sea and for their generosity in allowing us to join their excursions on the  F.
Data availability The raw Pool-seq data generated in this study have been deposited to the NCBI Short Read Archive under BioProject ID PRJNA844002. The E. affinis complex (Atlantic clade) draft genome and the Baltic pseudo-reference genome are available on Dryad (https://doi. org/10. 5061/dryad. r7sqv9sdz) 126. Study information has been deposited to BCO-DMO (https://www. bco-dmo. org/project/816918). Allele frequency data (SNP and haplotype block) are available on https://github. com/TheDBStern/Baltic_Lab_Wild.
These findings suggest that supplementary policies might be needed to reduce automobile reliance and stimulate demand for regional transit. Many transit researchers have conducted demand elasticity studies to identify what factors are significantly associated with transit ridership. However, no researcher has conducted this type of analysis specific to the GO Transit rail system. The purpose of this thesis is to fill this gap. Through literature review, variables significantly associated with transit demand were first identified. Station-level datasets were then compiled at monthly intervals from January 2016 to December 2019. During this process, station catchment areas estimated using PRESTO smartcard data were used to extract data related to land use, socioeconomic, and demographic indicators.
Additional factors related to station access, service quantity, and availability of substitute transport modes were also compiled. A random effect linear panel data estimator was then applied to obtain demand elasticity estimates. Of the variables included in the analysis, this study finds that several variables such as service quantity, population density, fuel price, and unemployment rate are significantly associated with transit demand, regardless of trip type examined. Ridership was also responsive to employment density and seasonal variation, although differing signs were shown depending on trip type examined. Surprisingly, demand was relatively unresponsive to enhanced station access options, including park and ride capacity and the quality of feeder bus connections. The results suggest that policies in addition to the service quantity improvements as outlined in the GO Expansion Program should be considered to further increase system demand.
The chapter concludes with a summary of data collection methods, specifically those used to estimate station catchment areas, extract external variable datasets, and measure station accessibility indicators. Chapter 4 outlines the methodology employed to answer the research questions. Methods used to delineate station catchment areas, measure station accessibility indicators, and analyze the impact that various external and internal variable datasets had on GO Transit rail ridership are therefore justified in this chapter. Chapter 5 explores data analysis methods. First, the chapter outlines how linear extrapolation, the use of Geographic Information Systems (GIS), and various statistical software programs were used to compile the dependent variable and independent variable datasets that were analyzed.
Secondly, findings from previous transport behavior, demand, and/or mode share studies are summarized with the purpose of creating a list of variables that could be determinantal to transit demand in the study area. Where possible, attempts were made to focus on the inclusion of studies completed in the North American context, which also included commuter / regional rail ridership in their analysis. Finally, quantitative methods used to identify and extract various datasets are reviewed, with a unique focus on methods used to obtain feeder bus connection quality datasets and those used to delineate station catchment boundaries. The paragraph concludes by identifying relevant gaps in the literature to be filled by this body of work.
Advanced Methods While simple linear regression methods are adequate in assessing one-dimension datasets, more advanced techniques are often used for datasets that have a cross-sectional and/or time-series component. These datasets, otherwise known as panel or longitudinal data, observe the behavior of multiple entities across time  (Torres-Reyna, 2007). Compared to a one-dimensional dataset, such as a study that analyzes station-level ridership at a given point in time, panel data is usually preferred as it provides more information, contains more variability, and demonstrates less collinearity amongst independent variables. When transit demand studies are considered, panel data also allows the researcher to utilize readily available internal variable datasets, and combine them with more detailed station level observations such as land use information, parking availability, and the provision of feeder bus connections, so that ridership figures can be explained more efficiently  (Guerra & Cervero, 2011).
When analyzing panel datasets, the simple OLS method is rarely used as subsequent observations inherently influence each other. Additionally, the impact of unobserved factors also has a consistent impact on model performance  (Torres-Reyna, 2007). However, various methods that build upon the OLS method are commonly used to analyze panel datasets which account for these issues, including pooled OLS, fixed effect and random effect estimators  (Wooldridge, 2012). A variety of statistical tests, such as the Lagrange Multiplier and the Hausman Test, are then applied to the model outputs to determine the method that best suits the dataset  (Guerra & Cervero, 2011; Lee & Lee, 2013; R. Liu, 2018; Stover & Christine Bae, 2011).
A statistically significant, yet substantially smaller demand elasticity of 0. 02 was identified in a demand elasticity study of 67 urbanized areas throughout the United States, but the authors theorize their results could be minimized by the fact that annual data was used to record this variable, whereas the majority of other variables included in the dataset were made available at monthly intervals  (Lee & Lee, 2013). The literature suggests that younger people are expected to use transit if alternative travel options are not readily available, while older people could be more likely to use transit in off-peak periods due to discretionary related travel patterns.
Delineation of Station Catchment Areas According to the literature, station catchment areas are used to extract external variable datasets included in demand elasticity studies. A station catchment area is defined as a spatial boundary that encompasses the area where the majority of non-transferring passengers originate from  (Andersen & Landex, 2008). In other words, the station catchment area can be viewed as the customer base for a given transit station or network. When using household statistics to formulate external variable datasets, station catchment areas should be as accurate as possible to ensure that data being captured is reflective of the customer base. Station catchment areas are delineated using a variety of methods. Most commonly, station catchment areas are determined as a function of the maximum distance a transit user is willing to reach.
In North America, this is commonly done by implementing a circular Euclidian buffer around a given station, ranging in distance from 400-800 metres  (Andersen & Landex, 2008; Durning & Townsend, 2015; El-Geneidy et al. , 2014; Guerra et al. , 2012). The size of the catchment area reflects the assumption that a passenger walking at a speed of 1. 3 metres per second can reach the station within ten minutes  (Guerra et al. , 2012). External variable datasets lying within the buffer are then extracted for further analysis. Network buffers are also commonly used to delineate station catchment areas. Unlike Euclidian buffers, aspects of the urban environment that might impede station access are incorporated into the estimate  (Andersen & Landex, 2008). Therefore, distribution and layout of road networks, pathways, buildings, rivers, and other natural / built features are incorporated to generate a more realistic station catchment area that is not uniform in size or shape.
Both Euclidian and network buffers can be estimated in terms of access time, rather than access distance, if the researcher chooses to do so. A study of 21 transit systems across the United States was conducted to determine if demand model accuracy differed on the Euclidian distance used to extract external variable datasets  (Guerra et al. , 2012). Data was extracted from 1,449 station catchment areas in 21 American cities, using buffers ranging in size from 0. 25 miles to 1. 5 miles. Model results did not improve depending on the size of the Euclidian buffer used, indicating that a "correct" station catchment area size is far from clear, and likely varies with the spatial context of the study. The authors state that based on these findings, researchers should use datasets that are easily calculatable and readily available when extracting external variable data.
Therefore, the authors recommend that distance decay-functions should be applied to external variable datasets, as their results indicate that data extracted from areas in close proximity to the station is more characteristic of the customer base. A direct demand analysis of transit riders in Madrid, Spain incorporated a distance decay function while extracting external variable data for use in their study  (Gutiérrez et al. , 2011). Consistent with  Zhao et al. (2003) , they stated that external variables extracted from station catchment areas should not be weighted equally, as households in close proximity to the station have a larger impact in explaining demand. Several demand models were tested using a variety of station catchment area delineation methods. They found that using a network distance threshold, coupled with a distance-decay function, improved model fit by 5.
The authors found that gender, age, park and ride capacity, overall trip length, and service quantity / quality variables were statistically significant in determining station access distance. For example, the station access distance of males was found to be 12. 5% longer compared to females, while an additional 100 park and ride spaces at the station level resulted in an increased access distance of 0. 38%. Further to this, a GIS analysis of transit riders found that station access distance was largely a function of station access mode  (Wang et al. , 2016). Customer origin points were collected for over four thousand public transit users after a survey was administered in Beijing, China. GIS tools were applied to this dataset to determine the most likely station access distance and associated access mode used by the sample.
Therefore, buffers estimated using alternative methods are needed to ensure that the majority of riders and associated external variable datasets are captured. Instead of using arbitrary buffers that are larger in size, Engel et al. (  2014 ) outlines a methodology that uses customer origin data to formulate station catchment areas to ensure that the size and shape of the buffer is representative of customer origin locations. In their analysis, the authors gathered customer origin point data via station level surveys, and mapped customer origin points using GIS tools. After the removal of outliers (ex. customer origins located more than 10km from the station, and an additional 10% of observations located farthest from the centre mean of observations) a convex hull polygon was digitized around these points.
However, a demand elasticity study has not been conducted in the context of the Greater Golden Horseshoe that: 1. Calculated demand elasticity estimates specific to commuter rail ridership, 2. Evaluated how commuter rail demand responds relative to trip type, 3. Considered the impact that various station accessibility indicators may have on demand, 4. Utilized a longitudinal dataset to incorporate both cross-sectional and temporal information into the analysis. Further, the Canadian Urban Transit Association recommends that when specific research questions relating to transit demand are proposed, studies that analyze a specific mode, geography, and trip type are most effective as they include and account for factors and variables specific to the regional context being examined (E. J. Miller et al.
Notably, the influence, significance, and importance of these relationships were shown to vary depending on mode examined, regional context of the study, trip type, and data availability. The results suggest that a demand elasticity study specific to the study area is needed in order to accurately answer the research questions proposed in Section 1. 5. A complete list of all studies summarized in this section of the literature review can be found in Appendix B. This resource could be used by transit researchers interested in identifying demand elasticity research that has previously been conducted. The literature review further summarized data collection methods commonly used in transit demand studies. Specifically, methods used to delineate station catchment variables were summarized, as the accuracy of external variable datasets and corresponding model performance is dependent on the method selected by the researcher.
Commonly, station catchment boundaries are digitized using Euclidian or network-based buffers, but inaccurate datasets could be captured if the station access distance of the customer base is extensive. Since this is the case for commuter rail systems, alternative methods such as the use of customer origin data to delineate station catchment boundaries is recommended. Methods used to represent station access indicators, including feeder bus connections, were also summarized. Notably, the literature states that this variable is commonly represented using a dummy variable, and other aspects that could be determinantal to use and subsequent ridership are understated. The use of an indicator that incorporates the quality and accessibility of feeder bus routes is therefore recommended. Various gaps in the literature were then identified.
Time Parameters and Trip Types Analyzed Data was recorded at monthly intervals, resulting in a total of 48 observation periods. Data collection was further disaggregated by time of boarding so that trip type could be controlled for. Therefore, separate datasets and models were created for the a. m. peak, midday off-peak, p. m. peak, and evening offpeak time periods. The time parameters as indicated in Table  4  were used to delineate these datasets. An observation was only included in the analysis if outbound service was offered at the given station at a given observation period. All stations in the study area offer outbound service during the a. m. peak period; therefore, all 61 stations were included in the a. m. peak period analysis, resulting in a balanced panel. Fewer stations were included in the midday off-peak and evening off-peak models, as two-way, all-day service is currently only provided to stations located along the Lakeshore West and Lakeshore East corridors.
Model Assumptions When linear regression analysis is conducted utilizing the OLS framework, several conditions should be met to ensure that model outputs are efficient, unbiased, and accurate  (Wooldridge, 2012). Otherwise known as the Gauss-Markov assumptions, these include: • Linearity, • Random Sampling, • Non-Collinearity, • Exogeneity, • Homoscedasticity. If these assumptions are not satisfied, inaccurate model outputs could be produced. For example, when multi-collinearity amongst two independent variables is present, this indicates that both factors are correlated with each other in addition to the dependent variable. In other words, this introduces redundancy into the model, as the same relationship is being captured by two independent variable datasets. When this occurs, the significance of the true relationship can be understated, as some the variance in the dependent variable is captured by a redundant supply of information.
Homoskedasticity Var(u|x) = σ $ Assumes that the variance of the error term is the same given any value of the independent variable (s). Coefficients could be overestimated. Model will be less efficient, but unbiased. Wooldridge (2012)  further states that when panel datasets are analyzed, the structure of the data results in the inherent violation of various Gauss-Markov assumptions. For example, the assumption of random sampling is fundamentally violated, as data points are continuously obtained from the same entities over a defined time-series. Further, this leads to the violation of homoskedasticity, as values obtained in an observation period naturally depend on conditions observed in a prior observation period. Therefore, serial autocorrelation and spatial dependencies often arise. When analyzing panel datasets, it is common practice to first ensure that multi-collinearity is not present to ensure that coefficients and relationships are accurate.
Pooled Ordinary Least Squares Pooled OLS is the most simplistic approach used when modeling panel data, as a simple OLS regression is run on all observations included in the dataset. This approach is commonly selected when an unbalanced panel is being analyzed, or when sample entities vary greatly throughout the time-series  (Wooldridge, 2012). Notably, assumptions regarding individual heterogeneity are ignored using this approach, meaning that spatial and temporal effects which could impact the true nature of the relationship between the independent variable(s) and dependent variables are not accounted for. As a result, many pooled OLS outputs appear to be good, as statistically significant coefficients, expected relationships, and large R-squared values are computed (Gill-Carcia, JR. Puron-Cid, 2014).
Selection of Appropriate Estimator When selecting the appropriate estimator, it is common practice for the researcher to analyze the panel dataset using both pooled OLS, fixed effect, and random effect models. A variety of statistical tests are then applied to the model outputs to determine the method that best suits the dataset  (Guerra & Cervero, 2011; Lee & Lee, 2013; R. Liu, 2018; Stover & Christine Bae, 2011). Of these, a Breusch-Pagen Lagrange Multiplier is commonly used to compare the pooled OLS estimator to both the fixed and random effect estimators. The null hypothesis is that there are no panel effects; if the test produces a significant result, this indicates that time and/or entity effects are present. Therefore, the use of a fixed or random effect estimator will produce more reliable and unbiased results and should be further explored by the researcher.
Controlling for Heteroskedasticity It is also common practice for the researcher to further diagnose the model for the presence of spatial and/or temporal dependencies, as the presence of such relationships would result in inefficient model outputs. As mentioned in Section 4. 3. 2, it is common that spherical errors such as heteroskedasticity and autocorrelation are present within panel datasets due to the sampling nature. Various test statistics can be applied to detect these effects, including the Durbin-Watson test for serial correlation in panel data models and the Breusch-Pagen test for heteroskedasticity. If detected, non-constant variance estimates can be used to compute regression outputs that are robust to heteroskedasticity and autoregressive effects. Regression outputs can be estimated using a variety of non-constant variance estimators, however White robust standard errors are commonly used in econometric analysis and transit demand elasticity studies that have utilized a panel data approach  (Lee & Lee, 2013; Torres-Reyna, 2007 , 2010).
Dependent Variable As outlined in Section 2. 2. 3, specific and accurate ridership counts are recorded by Metrolinx via the PRESTO smartcard system. Therefore, the number of boardings as indicated by the PRESO system was used to formulate the dependent variable dataset for this study. Filters were applied so that monthly boarding counts could be obtained at the station-level, separated by trip type. Only weekday ridership counts were included in the analysis. Station-level monthly ridership is therefore expressed by the following equation: ;<=>ℎ@A BCDEFGℎCH %& = I J<KFDC=LG ' ( ') " M@ ∀ G>K>C<=G, @ K=D O<=>ℎG, M Eq. 9 Where: • * = a given month, • + = a given station, • , = a given weekday, • -. '/0,123 = number of boardings as recorded by PRESTO, • ∀ = for all. number of boardings during the a.
Observations were then uploaded to ArcGIS so that station catchment areas could be digitized. Current GIS software does not allow for postal code data to be georeferenced if corresponding spatial information, such as latitude and longitude coordinates, are not specified. Instead, observations need to be linked to a baselayer where postal code locations have already been georeferenced. As shown in Figure  5 , a shapefile containing all possible postal code addresses and associated locations in the study area was downloaded from the University of Waterloo's Geospatial Centre so that customer origin data could be georeferenced to the appropriate location. In some cases, it was found that a single postal code address was represented by multiple polygons. This occurred if postal code addresses were separated by a natural or urban feature, or if one postal code address was used to represent multiple parcels of land within the study area.