dataesr/scientific-paragraphs-categorization huggingface dataset:
We investigate the impact of local environment on the galaxy stellar mass function (SMF) spanning a wide range of galaxy densities from the field up to dense cores of massive galaxy clusters. Data are drawn from a sample of eight fields from the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. Deep photometry allow us to select mass-complete samples of galaxies down to 10 9 M. Taking advantage of >4000 secure spectroscopic redshifts from ORELSE and precise photometric redshifts, we construct 3-dimensional density maps between 0. 55 < z < 1. 3 using a Voronoi tessellation approach. We find that the shape of the SMF depends strongly on local environment exhibited by a smooth, continual increase in the relative numbers of high-to low-mass galaxies towards denser environments.
Indeed galaxy groups are believed to be the environment most conducive to galaxy-galaxy merging due to their moderate velocity dispersions, whereas velocities in clusters may act to suppress merging despite bolstering elevated number densities of galaxies  (Lin et al. 2010). In this paper we present new measurements of the galaxy SMF as a function of local environment from the Observations of Redshift Evolution in Large-Scale Environments survey (ORELSE  Lubin et al. 2009). ORELSE is a wide-field survey dedicated to studying galaxy evolution across the full range of local environments with extensive photometric and spectroscopic observations of multiple wellknown galaxy overdensities at 0. 6 < z < 1. 3. In the work presented here we make use of a subset of eight ORELSE fields from the full sample for which all data have been currently reduced.
Photometry Each LSS field comprises a wealth of photometric data spanning optical to mid-infrared wavelengths. These imaging data were compiled from both proposed observing campaigns part of ORELSE as well as archival data from Suprime-Cam  (Miyazaki et al. 2002)   (IRAC:  Fazio et al. 2004 ) on the Spitzer Space Telescope. Table  2  lists the available photometry for each field, which facilities and instruments were used, as well as depth estimates. Optical and Y-band imaging from Subaru Suprime-Cam was reduced using the SDFRED pipeline 1   (Ouchi et al. 2004)  as well as several Terapix 2 software packages. Basic image processing is first done with SDFRED which we use to perform (1) bias level subtraction based on designated overscan regions on the Suprime-Cam CCDs, (2) flat field correction, (3) optics and atmospheric distortion correction, and (4) equalization of the point spread function (PSF).
Flat fields were constructed directly from the science images by masking out objects to create "super sky flats". This is possible due to the nature of the observations which include many individual dithered images and because we do not observe large extended objects. Next we perform astrometric alignment, photometric scaling, and image stacking using the Source Extractor (SExtractor:  Bertin & Arnouts 1996) , Software for Calibrating AstroMetry and Photometry (SCAMP:  Bertin 2006) , and SWarp  (Bertin et al. 2002)  software packages to create fully-reduced mosaics and weight maps. Photometric calibration is performed from same-night observations of standard star fields from the  Landolt (1992)  catalogs. Standard fields are reduced identically as described above. Optical imaging from the Large Format Camera (LFC) mounted on the 200-inch Hale Telescope at Palomar Observatory is reduced using a suite of scripts 3 written in Image Reduction and Analysis Facility (IRAF).
These scripts perform standard procedures for image processing (e. g. bias subtraction, flat field correction) as well as a few routines written specifically for handling LFC data. We refer the reader to  Gal et al. (2005)  for a more detailed discussion of the processing of LFC images. Near-infrared imaging in the J and K/K s bands were taken using UKIRT/WFCAM and CFHT/WIRCam. Both facilities implement automated data reduction pipelines that output fully-reduced mosaics and weight maps. We photometrically calibrate these mosaics using bright (<15 mag), non-saturated objects with existing photometry from 2MASS in each field. It is important to note that the 2MASS K s filter is significantly different than the WFCAM K filter. In order to account for this we make use of infrared spectral observations of stars from the Infrared Telescope Facility (IRTF) spectral library  (Rayner et al.
1 of  Whitaker et al. 2011). Fluxes are corrected for attenuation from dust in the Milky Way based on the reddening maps derived by  Schlafly & Finkbeiner (2011)  as provided by the NASA/IPAC Infrared Science Archive 4 (IRSA). We define an aperture correction as the ratio of an object's total flux to its aperture flux in the PSF-matched detection image, where we use SExtractor's AUTO flux as the "total" flux. All of our cluster fields have non-cryogenic Spitzer/IRAC imaging  ([3. 6 ] and [4. 5]µm). However, two fields (RXJ1716 and SC1604) were observed during the cryogenic mission and thus have [5. 8] and [8. 0]µm imaging. We process the individual corrected basic calibrated data (cBCD) images using the MOPEX software package  (Makovoz et al. 2006 ) along with several custom scripts. We first preprocess the cBCD frames using a custom IDL code (J.
Surace, private communication) which performs and improved correction for column pulldown and muxbleed artifacts. The images are next background-matched using the overlap. pl routine from MOPEX prior to mosaicing with the mosaic. pl routine. All Spitzer imaging is flux-calibrated and provided in units of MJy/sr. Due to the significantly larger PSFs of Spitzer/IRAC images (∼ 2 ) we take an alternate approach for measuring photometry. We use T-  PHOT (Merlin et al. 2015) , a software package designed to extract photometry in crowded images where blending from neighbours is significant. In brief the methodology of T-PHOT is as follows: first, the positions and morphologies of objects are obtained for use as priors based on a segmentation map (produced by SExtractor) of a higher-resolution image, in our case the detection image mentioned above.
Spectroscopy The bulk of the spectroscopic data used in this study were obtained as part of a massive 300 hour Keck 2/DEep Imaging Multi-Object Spectrograph (DEIMOS;  Faber et al. 2003)  campaign targeting all fields of the ORELSE survey listed in Table  1  of  Lubin et al. (2009). In this section we briefly describe the targeting, acquisition, and reduction of those data pertaining to the eight fields presented in this study. A total of 67 slitmasks were observed over the eight fields, with the number of slitmasks per field ranging from four (RCS0224) to 18 (SC1604). Slitmasks were observed with the 1200 line mm -1 grating with 1 slits resulting in a plate scale of 0. 33 Å pix -1 , an R ∼ 5000 (λ/θ FWHM , where θ FWHM is the full-width half-maximum resolution), and a wavelength coverage of ∆λ∼2600.
2009 , for a full description). Indeed, as was shown by  Shen et al. (2017) , the ORELSE spectroscopic sample is broadly representative of the underlying galaxy population over a wide range of stellar masses and colours. Data were reduced using a version of the Deep Evolutionary Extragalactic Probe 2 (DEEP2;  Davis et al. 2003; Newman et al. 2013)  spec2d pipeline modified to improve the precision of the response correction, perform absolute spectrophotometic flux calibration, and improve the handling of the joining of the blue and red ends of the spectra over the ∼ 5 Å gap that separates the two CCD arrays. All objects, those targeted and those which serendipitously fell in a slit, were visually inspected and assigned a spectroscopic redshift (hereafter z spec ) and a redshift quality code (Q) in the zspec environment following the DEEP2 scheme (see  Gal et al.
2008; Newman et al. 2013). The process for finding and extracting serendipitous detections and assigning photometric counterparts is detailed in  Lemaux et al. (2009). Spectroscopic redshifts were assigned a quality code of Q=-1 (stars) or Q = 3, 4 (galaxies) were considered secure. To be identified securely as a star we required either the presence of multiple significant narrow photospheric absorption features (typically Hα and the Ca 2 triplet) or obvious broad continuum features indicative of a late-type star (primarily TiO). An extragalactic spectroscopic redshifts was considered secure only in the presence of two or more emission or absorption features, where Q = 3 indicates that one or more of the features was slightly questionable in S/N or for some other reason. The two components of the λ3726 Å, λ3729 Å [O 2] feature were considered sufficient to assign a spectrum Q = 4 when unblended as long as both components were significantly detected.
A quality code of Q = 3 was assigned when the two components moderately blended by velocity effects in the absence of additional corroborating features. It has been shown that adopting this scheme and these quality code cuts results in extragalactic redshift measurements that are reliable at the > ∼ 99% level  (Newman et al. 2013 ). For more details on the meaning of the quality codes and the likelihood for a redshift of a given quality code to be accurate see  Newman et al. (2013).
Spectral Energy Distribution Fitting We perform spectral energy distribution (SED) fitting on the observed optical through mid-IR photometry in order to derive photometric redshifts as well as physical properties (e. g. stellar mass, A V ). In estimating photometric redshifts we employ the code Easy and Accurate Redshifts from Yale (EAZY:  Brammer et al. 2008). For this we use fixed-aperture fluxes measured from PSF-matched images. Briefly, EAZY utilizes a set of 6 basis template SEDs derived from a nonnegative matrix factorization decomposition of the Projet d'Etude des GAlaxies par Synthése Evolutive template library (P ÉGASE: Fioc & Rocca-Volmerange 1997), as well as one additional template representing an old stellar population from the  Maraston (2005)  library (this last template was added in order to help accommodate evolved galaxies at z < 1 which are not well-fit by the 6 basis templates).
To assess the precision and accuracy of these photometric redshifts we compare to our spectroscopic redshift measurements. In Figure  1  we plot z phot vs. z spec for the sample of galaxies with high-quality spectroscopic redshifts. For each field we fit a Gaussian to the distribution of the residual (z photz spec )/(1 + z spec ) for all objects and take the best-fit σ ∆z/(1+z spec ) as the z phot uncertainty. Values of σ ∆z/(1+z spec ) , ranging between 2. 2-3. 2%, are shown in Figure  1  and Table  3. We next utilize the code Fitting and Assessment of Synthetic Templates (FAST:  Kriek et al. 2009 ) for deriving stellar masses and other physical properties. Here we use aperture-corrected fluxes and fix galaxies to their derived photometric redshift (or secure z spec when available). In brief, FAST creates a multi-dimensional cube of model fluxes from a provided stellar population synthesis library (SPS).
Parameters of the best-fit single-and double-Schechter functions and corresponding BIC are shown in Table  4. An important subtlety, however, is that to robustly discriminate the necessity of a double-Schechter parameterization requires having measurements below the threshold at which the upturn begins to dominate ( 10 9. 5 M ) which, for quiescent galaxies in our dataset, lies near and/or below the stellar mass completeness limit. Therefore, a direct comparison of the BIC for quiescent SMFs does not carry the same weight as for the total or star-forming SMFs, and more-so does not rule out the potential existence of such an upturn in all cases. What else is immediately noticeable is that, similar to the full galaxy sample, the shape of both the star-forming and quiescent SMFs are strongly dependent on local environment, in agreement with some recent studies  (Vulcani et al.
ACKNOWLEDGEMENTS This material is based upon work supported by the  National Aeronautics and Space Administration  under  NASA Grant Number   NNX15AK92G. Part of the work presented herein is supported by the  National Science Foundation  under Grant No. 1411943. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013). A. R. T. would also like to thank  Chris Fassnacht  for aiding in the reduction of photometric data through helpful discussions and provision of well-documented code. S. M. acknowledges financial support from the  Institut Universitaire de France (IUF) , of which she is senior member. A portion of this work made use of the Peloton computing cluster operated by the  Division of Mathematical and Physical Sciences  at the  University of California, Davis.
Warming and CO 2 fertilization effects stimulate vegetation growth and increase land carbon storage in all three land surface model configurations (Fig. 5 , top row panels). This results in an increase of vegetation carbon of between 10 and 60 Gt C by 2100 with a greater increase in ORCHIDEE-MICT than JULES and a greater increase for the higheremissions scenarios, due to higher atmospheric CO 2. Ito et al. (2016)  used offline land surface models driven by weather data from global climate models under a high-emissions scenario and showed the vegetation carbon change was between -5 and 80 Gt C: a much larger spread than found here. Qian et al. (2010)  assessed the C4MIP (Coupled Climate Carbon Cycle Model Intercomparison Project) models under a high-emissions scenario and found an increasing vegetation carbon of 17 ± 8 Gt C by 2100 -this range falls within the spread shown here.
Acknowledgements. The authors acknowledge funding and support from the  Permafrost in the Arctic and Global Effects  in the 21st century ( PAGE21) Seventh Framework Programme  project  GA282700. Eleanor J. Burke  was supported by the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ) and  CRESCENDO  ( EU  project  641816 ). Chris Huntingford acknowledges the  NERC CEH science budget. Sarah E. Chadburn  is grateful to the  University of Exeter  for access to facilities and was supported by the  Joint Partnership Initiative  project  COnstraining Uncertainties in the Permafrost-climate feedback (COUP)  ( National Environment Research Council  grant  NE/M01990X/1 ). Edited by: K. Thonicke Reviewed by: four anonymous referees.
Background: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets.
Correspondingly, the challenge has now shifted to the analysis of these large data sets, essentially shifting the bottleneck from data collection to data analysis, and motivating the development of new data analysis methods. A number of software tools exist for analyzing genotypephenotype associations. One of the most popular tools for analyzing GWAS results is the open source software PLINK  [5] , which provides a number of analysis functions, including logistic regression to associate genetic variants with binary phenotypes. However, for today's large genotype-phenotype datasets, association analyses can take many hours for a single phenotype. A number of groups have described alternative algorithms and software for more rapid computation of associations between genetic variants and phenotypes, often motivated by detecting epistasis  [6] [7] [8] [9] [10].
Methods Fig. 1  outlines our iterative approach using prize-based crowdsourcing to speed up GWAS analysis. Our workflow started with profiling of the PLINK 1. 07 application, and then proceeded via contest-based crowdsourcing to accelerate logistic regression. Faster logistic regression code was re-integrated into PLINK 1. 07, and then donated back to the PLINK2 open-source project. In addition, we crowdsourced data input/output changes and multithreading work and used coarse-grained parallelization to achieve further accelerations.
Datasets and nomenclature In this paper we use the following symbols to summarize the dimensions of genotype datasets: r N, the number of subjects r M, the number of genetic markers (variants) r P, the number of phenotypes r C, the number of covariates Our motivating use case was a GWAS dataset from the COPDGene consortium  [17]  with N = 6678, M = 645 863, P = 164, and C = 7. The seven covariates were five principal components computed from the genotype matrix, age at study enrollment, and smoking status (in pack-years). Test data sets for the contest were derived by sampling genotypes from a 1000 Genomes Project  [18, 19]  that included 1624 individuals from 8 populations with 100 000 markers per subject, then generating simulated phenotypes and covariates corresponding to the genotypes.
For each test dataset, the 4 problem dimensions were uniformly sampled from these ranges: N 500-1500; P 3 and 50; M 1000-5000; and C 5 and 10. A genotype matrix (NxM) was then sampled from the 1624 × 10 000 genotype matrix. For each phenotype, a liability value for each individual was computed assuming 0-5 % of markers had nonzero phenotypic effects on a background of population effects, and the binary phenotype was set to 1 when the liability value was greater than zero, and zero otherwise. Finally, the covariate vectors were set to be the first C principal components of the normalized genotype matrix. Details of the genotype and phenotype simulation framework were included in the contest problem statement  [20].
Compute environments Our routine compute environment consisted of a highperformance compute (HPC) cluster of about 2300 processors running the LSF job scheduler. Typical processors were Intel Xeon E7-8891 V2 (64 bit, 3. 2 GHz), and nodes had 529GB Iterative open source contests to accelerate logistic regression for GWAS analysis. Workflow and code outputs of our project. First, a 10-day marathon crowd sourcing competition was hosted to accelerate the logistic regression code from PLINK 1. 07, yielding code C1. Accelerated logistic regression code was integrated back into PLINK 1. 07, yielding code PLINK-FLR. The logistic regression elements were donated and integrated into the PLINK2 project. A first-to-finish contest was run to speed up data initialization in the C1 code, yielding code C2.
Another first-to-finish contest was run to multithread the C2 code, yielding code C3. C3 was then combined with coarse-grained HPC parallelization and PLINK-FLR, yielding mPLINK. RAM. The operating system was Red Hat Enterprise Linux 6. 5. The HPC environment was shared with multiple users across our organization, making it more difficult to capture consistent benchmark times. So additional testing, as noted in the Results section, was done on an Amazon Web Services (AWS) m4. 4xlarge instance running the same operating system as HPC (Amazon machine instance ID = ami-6869aa05).
Logistic regression contest To initialize our contest, a reference implementation of the PLINK 1. 07 logistic regression code was created in a test harness suitable for contestants with no prior knowledge of genetics. The core regression code from PLINK was extracted and repackaged into a C++ class with a single public method called computeAssociations(). To eliminate the need for contestants to work with PLINK-specific file formats and data structures, the class was designed to read SNP data from a human-readable text file containing allele dosages, instead of PLINK's more compact but opaque. bed files. A scoring metric that supported our goal of achieving both improved accuracy and speed was created and used as the sole metric to award prizes. The accuracy score component was calculated using the following procedure: Contestants computed the M x P matrix of chi-square (Z) statistics, ordered in decreasing order of significance (i.
Contract development with logistic regression contest winner To enable PLINK users to take advantage of enhancements generated by crowdsourcing, we contracted for $2500 with the winner of the logistic regression contest to integrate his code with PLINK 1. 07. Given the contestants' deep familiarity with his own code, this was an efficient way to integrate contest code into PLINK 1. 07 with significantly less effort than would have been required by a third party. The integrated code module was then donated to the PLINK2 project.
Data input/initialization contest Contestants were provided with the complete source code of PLINK 1. 07 and a winning program from the logistic regression marathon contest. The focus of the contest was to revise the code to make the whole execution process faster by any means possible, but the contestants were directed to look specifically at the code that handled reading input data and initializing data structures (i. e. , the steps from reading input genotypes from disk, to the point where the in-memory data structures required for the contest algorithm to execute were created and loaded). Four test cases covering a range of problem sizes (N = 100 individuals with M = 5000, 50 000, and 700 000 markers; and N = 6000 individuals with M = 7000 markers) were provided to contestants. The largest test case was use to rank performance.
Multithreading contest In addition to novel approaches to logistic regression, which we sought in the logistic regression contest, we also used crowdsourcing to acquire resources to do more "conventional" coding work, such as multithreading. Contestants were provided with source code created by winning contestants in the data input/initialization contest and asked to multithread this code. The contest  [22]  was hosted on Topcoder. com. For testing, the number of threads was set to 4 and a successful solution required at least a 2-fold decrease in processing time. The prize consisted of two parts. The first contestant to achieve a 2x speedup with multithreading won $600. The winning code was then made available to all other contestants. At the end of 2 weeks, the contestant with the submission with the fastest speed qualified for a prize of min ($100k , $1000), where k is the additional fold-increase by which the code was accelerated, relative to the first winner's code.
Coarse-grained parallelization As a final step, we implemented coarse-grained (scatter-gather) parallelization using codes generated from the project. Multiple parallel processes running fast logistic regression code were executed using the Platform LSF load scheduling software on a HPC cluster. The approach for coarse-grained parallelization was to run fast logistic regression on all input markers, identify those with significant association statistics, and then run an accelerated PLINK on the subset of markers with logistic association P values less than a user-selected threshold to generate the final regression summary statistics.