dataesr/scientific-paragraphs-categorization huggingface dataset:
Effects of pre-nursery-based QTL on field Ganoderma resistance in the La Mé parents The effects of the QTL identified using the pre-nursery data on GA×GB crosses were evaluated in the field where 219 LM parents included in the pre-nursery study were planted and underwent natural, uncontrolled Ganoderma infection. The time of the first Ganoderma symptom appearance (T1S) was modeled using Cox regression with the date of planting as covariate (P < 0. 01). The effect of the percentage of favorable alleles per individual among the 21 QTL regions identified in the LM genetic background (range 28-75%) was first assessed to evaluate the global trend between pre-nursery and field conditions. The percentage of favorable alleles effect was not found to be significant (P=0. 2), but Kaplan-Meier estimates of survival showed consistency between the pre-nursery and field QTL effects, a higher percentage of favorable alleles increased the probability of survival (Fig.
Opportunities and issues of QTL mapping using data from breeding programs Breeding programs for perennials are inherently geared towards long-term work with extensive data recording. This make them highly suited to the in silico approach, which is likely to improve the statistical properties of QTL detection through the increase in population size and diversity compared to conventional biparental populations. However, the specificity of the data from breeding programs, such as the extent of non-genetic effects due to longterm data or the genetic and phenotypic design unbalances due to the selection process, could reduce the expected benefits of QTL detection, namely its power and the accuracy of QTL location and QTL effect estimation  (Würschum 2012 ). Hence, these datasets require a first stage of statistical modeling to account for several nongenetic effects and to obtain genotypic values.
Thanks to their flexibility, mixed models are ideal tools to handle several types of data and effects  (Smith et al. 2005). We used two types of mixed models, LMM and GLMM that enabled us to predict the GCA of genotyped individuals while accounting for confounding effects. We subsequently used these GCA values in FlexQTL because this software requires only one value per genotyped individual whereas they were progeny tested in the pre-nursery trials. Such a two-stage approach could affect QTL results so one-stage approaches are preferred when possible  (Xue et al. 2017; Barrasso et al. 2019). The two types of mixed model used in this study did not lead to major differences in the QTLs identified, and a one-stage IBDbased variance component approach previously reported for production traits (IBD-VC,  Tisné et al.
2015 ) that we used on pre-nursery Ganoderma data also produced similar results (data not shown). However, the calculation time requirement for the IBD-VC is an obstacle to a proper estimation of the significance threshold by permutation and a multi-QTL mapping procedure, which made us favor the approach presented. Few studies have assessed the effects of the dataset features on QTL detection. In barley, using GWAS with an unbalanced dataset, the false positive rate was increased, whereas one-stage analysis performed better  (Wang et al. 2012). In durum wheat, a GWAS performed both on an unbalanced and balanced dataset from a breeding program showed major overlapping of selected SNP  (Johnson et al. 2019). In diploid potato, a dataset grouping F3 families under selection was analyzed using either GWAS, stratified linkage or IBD based approaches that led to consistent QTL detection, but revealed issues concerning the QTL allele frequencies that could affect the results  (Korontzis et al.
Among them, the T1S QTL at the bottom of LG1 collocated with a QTL identified in GB and LM populations in the present study. The Eg9PP population and a large-scale genetic trial involving GB parents related to the founder of the present study (NGP population,  Tisné et al. 2015 , Tisné et al. 2019)  were evaluated in the framework of the breeding program. Hence, data for fruit bunch production, oil extraction rate, and height increment traits were stored in databases, and both populations as well as the population from the present study were genotyped with the same SSR markers from a reference genetic map  (Cochard et al. 2015)  allowing QTL detection. We observed that among the six Ganoderma QTL regions with higher statistical support found in the GB, LM or YBI populations, most collocated with a large number of QTL for other agronomic traits  (Tisné, personal communication).
2014). A first concern is to insure the consistency of QTL effects between the pre-nursery and field results, like in conventional selection  (Durand-Gasselin et al. 2018). We attempted to assess this at the QTL level with the extensive use of the data from the breeding program, including the Ganoderma census routinely recorded on seed and genealogical gardens. Despite the limitations of the approach, i. e. analyzing mature palms of pure genetic backgrounds in the field vs GA×GB seedlings in pre-nursey and spatio-temporal heterogeneity in the field, the majority of QTL effect directions were consistent. Thus, the combination of favorable QTL alleles improved field resistance even non-significant and small magnitude effects that would not have been detectable in the field at this stage due to the lack of sufficiently powerful experiments.
Secondly, the quantitative nature of Ganoderma resistance identified could hamper the conventional QTL pyramiding approach due to the high number of loci involved, especially considering the long generation time in oil palm. In such a QDR context, the MAS approaches developed for other agronomic quantitative traits are probably more suitable, especially the genomic selection (GS) approach  (Poland and Rutkoski 2016). In oil palm, GS has emerged as an efficient MAS method and is being increasingly evaluated for yield improvement  (Nyouma et al. 2019). Thus GS statistical models and implementation modes already assessed in oil palm could be transferred or adapted to Ganoderma disease related data from the breeding program  (Cros et al. 2015 (Cros et al. , 2017)). However, the qualitative/quantitative nature of disease resistance is a continuum  (Poland et al.
Conclusion The cost-effective and efficient in silico mapping approach assessed in this study has great potential for the implementation of MAS of traits of interest in oil palm. Its application in the context of Ganoderma disease resistance enabled us to use the considerable quantities of data generated in the framework of conventional phenotypic selection to obtain valuable information in the MAS perspective. First, important information on the genetic architecture of resistance to Ganoderma disease was obtained, confirming its quantitative nature and identifying the loci involved. In addition, together with other ongoing works, this study sheds light on the relationships between Ganoderma resistance and yield related traits that could produce undesirable trade-offs. Second, the consistency between genetic resistance in pre-nursery conditions and in the field was assessed at the QTL level and globally indicated satisfactory portability.
Acknowledgments This study was based on a very intensive and laborious work involving many people in the long-term. We thank  Zulkifi Lubis ,  Augustiaman Purba ,  Shri Jeweyen , and all the  SOCFIN Indonesia  staff at Tanah Gambus who performed the pre-nursery trials. We thank the  PalmElit  staff,  Hubert de Franqueville  and  Michaël Pernaci  for information on plant pathology and Nicolas Turnbull on breeding. We acknowledge  Tristan Durand-Gasselin  ( PalmElit ) for his insightful review of the study and manuscript. We thank  Eric van de Weg  ( Wageningen UR ) for the review of the manuscript. This research was partly funded by a grant from  PalmElit SAS. MD contributed partly to this study while she was visiting researcher at  Georgetown University  and supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  No840383.
Tanah Gambus who performed the pre-nursery trials. We thank the  PalmElit  staff,  Hubert de Franqueville  and  Michaël Pernaci  for information on plant pathology and Nicolas Turnbull on breeding. We acknowledge  Tristan Durand-Gasselin  ( PalmElit ) for his insightful review of the study and manuscript. We thank  Eric van de Weg  ( Wageningen UR ) for the review of the manuscript. This research was partly funded by a grant from  PalmElit SAS. MD contributed partly to this study while she was visiting researcher at  Georgetown University  and supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  No840383.
announced at the EOSC Architecture group web page. We refer to this report as "the SIRS report" in the present document. The first version of our Open comments on the Task Force SIRS report. (Gomez-Diaz and Recio 2020b) has been disseminated as a Zenodo preprint dated November 2nd 2020. Now, a few months later, we have decided to present an updated version of our work, including some new reflections motivated by recent information on the SIRS report topics. Political evolutions in digital policy have been recently announced by Ursula von der Leyen, President of the European Commission  (Madiega 2020). They are designed to enhance Europe's strategic autonomy. In this context, and in order to contribute to and to support the Europe's Digital sovereignty, we have avoided the use of Google accounts as much as possible, and in particular, the direct editing of the above mentioned Google document.
Please note that this definition does not imply any difference between the concepts of software and research software, as the research software proposed term in the SIRS report could easily include, for instance, any version of the Windows operating system or a commercial scientific software such as Matlab and many other similar products. Moreover, according with this definition proposal, all software developed since 1960 should be considered as research software, as a science history researcher (for example) could feel that it should be preserved as an object for future studies. It seems to us that to define EOSC infrastructures and services based in this view of research software is a task that requires some essential precisions. Otherwise, using strictly the above research software definition, how could EOSC teams design adapted services? For which target community? In particular, it is necessary to pay careful attention when dealing with software produced by private companies.
It seems to us that these questions are not correctly presented or are missing in the report. On the other hand, The European Open Science Cloud (EOSC) is presented in (European Commission, Commission staff working document 2018) as follows: The EOSC will be a fundamental enabler of Open Science and of the digital transformation of science, offering every European researcher the possibility to access and reuse all publically funded research data in Europe, across disciplines and borders. In addition, we have proposed the following definition for Open Science in this recent preprint (Gomez-Diaz and Recio 2020a): Open Science is defined as the political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible, reusable. According with this Open Science vision, we propose another research software definition as follows:.
research software is a well identified set of code that has been written by a (again, well identified) research team. It is software that has been built and used to produce a result published or disseminated in some article or scientific contribution. Each research software encloses a set (of files) that contains the source code and the compiled code. It can also include other elements as the documentation, specifications, use cases, a test suite, examples of input data and corresponding output data, and even preparatory material. You can find this definition and all the considerations for its proposition in section 2. 1 of (Gomez-Diaz and Recio 2019). Thus, the research software definition in (Gomez-Diaz and Recio 2019) places correctly research software as a research output, usually produced within publicly funded research, and the role of EOSC efforts correspond to the definition and implementation of infrastructures and services to render research outputs, including research software, visible, accessible, reusable.
One of the authors of the present document provides with a good example to further study this research software concept. T. Recio is nowadays studying automatic proving of geometric theorems through dynamic geometry software, and comparing current work with the previously existing one, done with very old software computer programs, such as the computer language Logo or with more recent ones, such as the Cabri-geometry (commercial) mathematical application. The outputs of this research are currently being implemented in Geogebra. Under the SIRS report definition, all these four objects (Logo, Cabri-geometry, Geogebra, Geogebra developed software by T. Recio and collaborators) should be considered as research software, but, under the definition on (Gomez-Diaz and Recio 2019), only the last one fits well into this concept.
Research software is then the result of the ongoing scientific work, and not the historical tool or the commercial software which are used or under study. The output and the producer are therefore correctly identified. The SIRS report should clarify if historical tools and commercial software should be considered as part of the objects for which EOSC should provide infrastructure and services, and how commercial software should be dealt with. Section 2. 2 Infrastructures participating in the Task Force (TF) This section presents summary sheets introducing the nine infrastructures that are represented in the SIRS report: three for the Archives category (HAL, Software Heritage, and Zenodo), three for the publisher category (Dagstuhl, eLife, and IPOL), and three in the aggregators category (OpenAIRE, ScanR, and swMath).
1. 4 Cite/Credit To cite research software and to give credit to the research software producers is a real issue at stake, we could not agree more. This is why the CDUR proposed research software evaluation procedure dedicates a whole step, the *17 Citation step, to this issue (Gomez-Diaz and Recio 2019), in order to contribute to evolutions of research evaluation and to raise awareness on this matter among researchers and other research key actors. Moreover, in order to cite research sofware, a reference or citation form should be established by their producers. You can find the description of the PLUME/RELIER software reference cards in the already mentioned presentation of PLUME at fOSSa 2009. Section 5. 1. 1 Interactions The SIRS report mentions that:. it is important to ensure a vertical interconnection between an universal software archive and scholarly repositories, for the latter to feed the universal archive (see Figure  5 ).
*11 *12 *13 *7 *8 Section 3. 2. 2 Publishers The SIRS report mentions that: *9 Section 3. 2. 3 Aggregators The SIRS report mentions here the OpenAIRE Research Graph. Please note that there is also the Software Heritage graph Dataset presented in *10 (Pietri et al. 2019) and funded by the Horizon 2020 research and innovation programme under grant agreement No 825328 (FASTEN). The Software Heritage graph dataset is available in multiple formats, which includes a public instance on Amazon Athena interactive query service for ready-to-use powerful analytical processing. *14 *15 Section 3. 1. 3AggregatorsTheSIRSreportindicates that:Another remarkable example is the catalog built by the Plume project in order to collect information about software that is useful for research activities(Plume,  2013): it maintains a collection of over 400 entries manually curated about software As presented in (Directorate-General for Research and Innovation (DG-RTD) of the European Commission and Guédon 2019), the identified four key functions needed by scholarly publishing are:• registration to establish that work had been undertaken by individuals or groups of researchers at a particular time, and thus their claim to precedence; • certification to establish the validity of the findings; • dissemination to make scholarly works and their findings accessible and visible; • preservation to ensure that the 'records of science' are preserved, and remain accessible, for the long term.
(2017b)  find a turnover in the strength of the metallicity gradient, such that both more massive and less massive galaxies show flatter metallicity gradients than galaxies with masses around 10 10 M. In addition, the sample from  Poetrodjojo et al. (2018)  only includes galaxies with stellar masses below 10 10. 5 M. As it is based on an extensive longslit spectroscopy campaign rather than IFU maps, the M12 study may lack the full mapping of metallicity across the galaxy discs, but it does benefit from having access to direct measurements of the cold gas contents of the galaxies through the GALEX Arecibo SDSS Survey (GASS) and CO Legacy Database for GASS (COLD GASS) surveys  (Catinella et al. 2010; Saintonge et al. 2011). Their finding is that metallicity gradients tend to be flat within the optical radius of the galaxies, but that the magnitude of any drop in metallicity in the outskirts is well-correlated with the total atomic hydrogen content of the galaxy.
In addition to the  Pettini & Pagel (2004)  prescription, we also use the  Marino et al. (2013)  O3N2 metallicity calibrator to make direct comparisons with other works (Sect. 4. 3). However, since the  Marino et al. (2013)  O3N2 calibrator tends to underestimate high metallicities  (Erroz-Ferrer et al. 2019) , we used the  Pettini & Pagel (2004)  calibrator for the majority of the analysis. The same data products derived with the same pipeline are available for the M12 galaxies. Thus, all the following analysis steps were performed for both the M12 and the new data. The analysis procedure described in the previous sections provided radial profiles of the gas-phase metallicity. The radial variation of these profiles was quantified by the slope of a linear fit to the metallicity as a function of radius.
As mentioned above, not all galaxies in our sample are equipped with all measurements and sometimes metallicity gradients could not be measured due to too few radial bins with sufficient emission line detections. Thus the samples to work with contain 81 (67) galaxies for metallicity gradients measured on the entire radial profile (only from data points outside of 0. 5 r eff,r ). Of each sample, we use 80 percent of the galaxies for training purposes and 20 percent to test the resulting model. Tests after the training showed that metallicity gradients can be predicted with a mean absolute error of 0. 06 (0. 07) dex r -1 eff,r for metallicity gradients measured on the entire radial profile (only from data points outside of 0. 5 r eff,r ), and the most relevant features for the prediction are log f HI and log µ in both cases.
Where available, the yellow squares show an expected average metallicity gradient; see the text for more details. Grey, open circles in the background, and teal filled circles show the data from this work for the case that metallicity gradients were measured from profiles without radial data points inside of 0. 5 r eff. The bins within which median gradients were measured, were set to be equidistant in order to mitigate any effects of different distributions of the global properties. We note that we use the M13 O3N2 calibration in this figure. (PP04, used in this paper) metallicity prescription are based on the O3N2 line ratio, their normalisation is slightly different. For a consistent comparison, we use the M13 O3N2 method in every figure that includes MaNGA data (and note in the figure caption when this is the case).
We combine the Pipe3D catalogue with the SDSS DR7 MPA-JHU catalogue 4 to obtain 50 percent Petrosian radii for all galaxies. Together with the stellar mass as given by the MaNGA team, we are thus able to calculate µ (see Sect. 2). In addition, we use the H i masses and upper mass limits as provided by  Masters et al. (2019). If we are only selecting those galaxies that have measured metallicity gradients, as well as at least an upper limit for the H i mass, a match in the MPA-JHU catalogue (for µ measurements), and which are within ±1. 5σ of the Catinella et al. (  2018 ) SFMS, we get a sample of 544 galaxies from the MaNGA data sets. For simplicity, we treat H i mass upper limits as their true value. In Fig. 8 , we show the different correlations between metallicity gradient and global galaxy properties (from left to right: stellar mass surface density, stellar mass and H i mass fraction).
As trimmed means and medians agree within the standard deviation, we only show the median gradients. We also use a second, more stringent percentile range (40-60) as a check of the initial, broader range. Although the num-4 https://wwwmpa. mpa-garching. mpg. de/SDSS/DR7/ bers of galaxies drop significantly for the 40-60 percentile cut, the mean and median gradients trimmed in this way agree well between the 40-60 percentile cut method and the 16-84 percentile cut method. This means that the mild cut is sufficient to estimate a robust mean. The resulting trends agree with observations in Fig. 7. In the MaNGA data, correlations between the metallicity gradients and these global properties can also be seen. In most cases, our metallicity gradients, which were measured without data points at radii smaller than 0.
A and Fig. A. 1). Belfiore et al. (2017b)  investigated the correlation between metallicity gradients and stellar mass based on MaNGA data and found that the steepest (most negative) metallicity gradients are measured for galaxies with stellar masses around 10 10 to 10 10. 5 M. Galaxies at lower and higher stellar masses have flatter radial metallicity profiles. This trend can also be seen in the middle column of Fig. 8. This is particularly interesting as the  Belfiore et al. (2017b)  results are not based on the Sanchez et al. value-added catalogue that we use on the present work.
Circles show the global H i mass fraction as a function of metallicity measurements outside of 0. 7r 90,r. The data points are colour coded according to their galactocentric radius normalised by r 90,r. We note that some galaxies have multiple metallicity measurements outside of 0. 7r 90,r and would thus appear multiple times. The number in the lower left corner provides the Spearman correlation coefficient. The numbers in the bottom left provide the Spearman correlation coefficient R and the number of galaxies used to calculated the statistic. For MaNGA we only used H i detections in the computation of R. The yellow and red line show our model for different ratios of H i to stellar radius, namely, 3. 3 and 5. 6, respectively. The underlying model of the dashed lines assumes an effective yield of 0.
Results: Local metallicity and global HI content We now focus on the correlation between local gas-phase metallicity measurements and the global H i mass fraction as found by M12. With the new NTT data presented here, we are able to build on the findings of previous works. Previously, M12 reported a correlation between the local metallicity at the edge of the stellar disc and the global H i mass fraction. In Fig. 10 , we added the data of the new low-mass galaxies and find that the correlation holds. For MaNGA galaxies, only local metallicity measurements at one effective radius are provided in the value-added catalogues. Together with all those galaxies from our sample, which have a metallicity measurement within ±10 percent of the effective radius in r band, the correlation between local metallicity around the effective radius and the global H i mass fraction is shown in Fig.