dataesr/scientific-paragraphs-categorization huggingface dataset:
Despite good performance in some parts of the region, SM2Rain-CCI v. 2 still suffers too many gaps in space and time across West African. • All P-datasets present spatial discrepancies in their statistical score suggesting the use of a spatial P-datasets' merging approach to take advantage from all available P-datasets across West Africa. It should be reminded that most of the considered 0. 1° grid-cells count with only one gauge to represent the observed precipitation. Because of spatial inconsistency between point (gauges) and spatially average (P-datasets) measurement, different conclusion regarding the P-datasets reliability, could have been drawn if more gauges had been available per grid-cells or if using P-datasets as forcing data for hydrological modelling. Additionally, the study is based on a single four years temporal window.
of the highest correlation score, at 0. 5. Therefore, its low KGE score appears to be influenced by its very high positive bias value of 2. 5. This is in line with observation made at the monthly time step and the dominant influence of the bias values on the KGE score. 2, TMPA- Adj v7, CMORPH-BLD v. 1, CMORPH-CRT v. 1, GSMaP-RT v. 6, and PERSIANN-Adj had KGE scores superior to 0. 4, with the best score achieved by MSWEP v. 2. 2 (KGE = 0. 52). Generally, non-adjusted P-datasets presented the lowest KGE values except for GSMaP v. 6. The KGE decreased from 0. 44 (GSMaP-RT v. 6) to 0. 35 for (GSMaP-Adj v. 6). Interestingly, PERSIANN-RT presented a negative KGE value but one.
The highest HSS score was achieved by CMORPH-BLD v. 1 (HSS = 0. 58) and MSWEP v. 2. 2 (HSS = 0. 55). The P-dataset ability in reproducing daily precipitation amounts decreased for increasing intensity. Two P-dataset groups measured differently events of more than 15 mm. day -1. The first group (CMORPH-CRT and BLD v. 1, GSMaP-RT and Adj v. 6, MSWEP v. 2. 2, PERSIANN-RT and -Adj, ARC- 2, and CPC and TMPA-Adj v. 7) was much more suited for reproducing high-intensity precipitation events than the second group (CHIRP, CHIRPS, CMORPH-RAW v. 1, JRA-55, JRA-55 Adj, PERSIANN- CDR, TAMSAT v. 3, TMPA-RT v. 7, WFDEI, MERRA-2, ERA-Interim, and SM2Rain CCI v. 2). It is worth mentioning that the first group includes (i) P-datasets with gauge-based calibration using daily data and (ii) P-datasets available at the sub-daily time step.
Four coders, two for each country, categorized data for our content analysis. All coders were trained research assistants who were native to the respective countries, thus allowing for a culturally appropriate interpretation of advertisements for each respective consumer culture. To facilitate a reliable coding process of the advertisements, we provided coders with detailed instructions about the coding process. These included definitions (see Table  1 ) of all elements to be coded, including examples for each, and a standardized coding template to be filled out. Overall, we achieved a high inter-coder agreement (93%) for France and India, respectively, with an average Cohen's kappa of 0. 850. Cohen's kappa ranges from 0. 659 to 0. 960 for the French dataset. For India, Cohen's kappa ranges from 0.
2%) than that of hedonic (67. 6%) or utilitarian (59. 9%) products, confirming H4b. To test H5-H8, we employed a series of analyses of variance to assess differences between the number of COO cues used in individual advertisements and origin, ethnicity, involvement, and product category. We find varying support for our hypotheses. The pooled dataset shows no support for the hypothesized differences (see Table  2 ) but some support for the expected differences on the country level (Tables  3  and 4 ). In France, the results of the independent sample t-tests indicate that foreign advertisements employ more COO cues than domestic advertisements on average (M foreign = 1. 66, SD = 0. 83; M domestic = 1. 26, SD = 0. 52; t(411) = 5. 949, p <. 001), in support of H5. For H6, we find a significant difference between product ethnicity and the number of COO cues used in individual advertisements (M high_ethnic = 1.
We therefore encourage research to explore this further, possibly starting with interviews with advertisers in the field. Finally, while our study derived propositions on the usage of COO in print advertisements from theoretical frameworks, we could not analyze the effectiveness of these propositions by linking them to outcomes such as attitudes, ad awareness, brand equity, and profitability or related variables such as brand trust and transparency  (Buell & Norton, 2011). Our sample consisted of all brands advertising in two countries over a one-year period, involving both global and local brands. While for global brands secondary data for some outcome variables are readily available, the majority of brands analyzed are small to medium-sized (global and local) for which no such measures are available.
Complete parent-offspring trios A total of 13,603 potential trios (one individual and its two potential parents) involving diploid individuals were tested and resulted in counts from 0 to 31,398 ME. The distribution of ME in all tested trios (Fig. 3 ) contained a large gap between 575 and 3448, and the first three trios with a count of ME greater than or equal to 3448 all suggested the same individual ('Cox's Orange Pippin') as the offspring and thus were not all credible. A total of 295 trios had a ME count below 600 and these were inferred as complete parent-offspring sets. Among these, 32 inferred parent couples shared between two and five offspring, resulting in small full-sib families, while 212 inferred parent couples had only one offspring in the dataset. The largest inferred full-sib family included as the parents 'Jonathan' and 'Cox's Orange Pippin' and comprised five offspring, namely 'President Boudewijn', 'Prinses Beatrix', 'Prinses Irene', 'Prinses Marijke' and 'Céres', the first four being documented to have been raised from these two parents in 1935 by 'Instituut voor de Veredeling van Tuinbouwgewassen' (IVT) in The Netherlands (28; Additional file 3: Table  S3 ).
Interestingly, two genotypes were inferred as parents in more than ten oriented duos, namely 'Reinette de Hollande' and 'Calville Rouge d'Hiver', with 19 and 11 inferred offspring respectively. The 330 non-oriented duos involved 415 genotypes, 75 of which were inferred to be involved in two or more duos. Notably, the three genotypes 'Reinette Franche', 'White Astrachan' and 'Saint Germain' were inferred to be involved in 57, 12 and 10 parent-offspring relationships respectively, as well as having been inferred as parents in nine, four and six trios. We found documentation regarding one or both parents of 25 of the individuals inferred as offspring in the oriented duos and our findings agreed with one of the   S4 ). The other parent was either absent from our dataset (for 'Pitchounette' and for 'Delrouval') or deemed to be false (e.
Grandparents-parent-offspring groups Using a first subset of 25 K SNPs, the distribution of ME in all tested groups with a potential grandparent couple, parent and offspring, showed a distinct gap between 10 and 35 (Fig. 4 ). Following initial liberal selection with ME fewer than or equal to 100, the distribution of ME using the 253 K SNPs set in the tested subset of potential grandparents-parent-offspring groups contained a gap between 85 and 312 (Fig. 5 ). The first eight groups with a ME count greater than or equal to 312 all suggested the same genotype as the offspring ('Cox's Orange Pippin') and thus again, were not all credible. A total of   S6 ). Two potential grandparent couples had more than one inferred grandchild while 19 had only a single inferred grandchild. Moreover, four of these potential grandparent couples also had one or two inferred offspring in the dataset (i.
These two cultivars were then inferred to have 244 and 38 offspring, respectively, within the second to seventh generation of the pedigree. All inferred offspring of 'Reinette de Saintonge' were derived through 'Reinette Franche', and a large part of these (77 out of 244) through 'Reinette de Hollande' resulting from the cross between 'Reinette Franche' and 'Reinette des Carmes'. In contrast, the two predicted full-sibs of 'Reinette de Hollande' ('Mabbott's Pearmain' and 'Adams's Pearmain') did not have any predicted offspring in the dataset explored. The founder 'Alexander' had inferred descendants over four generations, with 105 offspring predicted in total. Another founder with numerous inferred descendants (87) over four generations was 'Margil', mostly through 'Cox's Orange Pippin' inferred as one of its first generation offspring.
The two remaining inferred triploiddiploid pairings with a low number of ME and a large number of tri-hom/di-het SNPs involved, as diploids, the Fig. 6  Reconstructed pedigree of 'Hood's Supreme'. Cultivar names are in their short version (see Additional file 1: Table  S1 ). The coloring of the name bars indicates the level of information known for the individual(s) in the pedigree: blue, individuals with both parents known, cream, individuals with one parent known, dark green, founders. Relationships are represented by black lines as the mother and the father cannot be identified with our data. The figure was drawn using data extracted from Additional file 8 and the Pedimap software  [57]  individuals contributing a haploid gamete in the two inferred trios. These results are presented in Additional file 12: Table  S7.
Discussion In the present study, we utilized the dense SNP dataset available from the Axiom®Apple480K array to investigate parentage and reconstruct pedigrees in a large set of apple cultivars. We used a simple exclusion test based on counting Mendelian Errors (ME) in putative relationships. Complete parent-offspring trios as well as individual parent-offspring duos were investigated. Where only a single parent could be proposed, we investigated whether the parents of the missing parent could be identified through an additional grandparent-offspring relationship.
This work is likely to evolve in the future with new genotypic, passport and pomological data and no doubt, further questions may be raised regarding the trueness-to-type of some accessions. For example, we found that the parents inferred for accession 1948-737 from the NFC collection (MUNQ 1973), named 'Topaz', did not correspond to the recorded pedigree for this cultivar. Thanks to SSR genotyping (data not shown), the accession was also found to be different from another accession with the same name (MUNQ 1213). Thorough examination of collection records showed that 'Topaz' is a homonym that has been applied to at least two different cultivars historically. Thus an indication of their origin added to the name would enable an easier distinction between the two. Nonetheless, we consider the parentage assignation in this study to be robust with respect to the genetic material in question, and with respect to the majority of the well-known and well documented cultivars.
Software ME counts were performed with R-scripts which have been deposited on SourceSup (https://sourcesup. renater. fr/projects/outbredpedigree/). The R package "snpStats"  [71]  was used to upload the SNP data in R. The package "network"  [72]  was used to generate Fig. 2. The rosnerTest used to identify outliers for heterozygosity belongs to the package "EnvStats"  [73].
Acknowledgments The authors thank  Dr. Walter Guerra  and  Dr. Thomas Letschka  from  Laimburg Research Center, Italy , for providing leaf samples of 13 accessions (encoded 'Lb399_xxx') from Laimburg germplasm collection. The authors also thank  Dr. Tal Isaacson  from  Volcani Center, Agricultural Research Organisation, Israel , for providing two DNA samples (accession names ' Golden Dorsett ' and  'Hashabi  ( MH. 10-1 )') and information and references about cultivars ' Golden Dorsett ' and 'Anna'. The staff at the  GENTYANE  genotyping platform (especially  Dr. Charles Poncet  and  Anthony Theron ,  INRA, Clermont-Ferrand, France ) and at the  Unité Expérimentale Horticole  ( INRA, Angers, France ) are warmly acknowledged for producing additional SNP genotyping data and for maintaining the INRA apple germplasm collection, respectively.
What signals quality for digital startups seeking access to external finance? Analyzing a unique dataset from India, we investigate the impact of traditional quality signals (e. g. , the founders' years of experience, prior industry experience); networking signals (e. g. , degrees from elite educational institutes, the breadth of an entrepreneurial team); and digital signals (e. g. , the social media presence of the startup on multiple social media sites) on the access to venture capital financing. We find that while networking and digital signals positively impact access to financing, traditional human capital signals do not have any significant impact on the process.
niua. org/sites/default/files/resources/nasscom-start-up-report-2017. pdf. 3 Indian Institute of Technology, a premier engineering college. 4 Indian Institute of Management, a premier business college. 5 Birla Institute of Technology, a premier engineering college. 6 See the full report on at: https://www. kalaari. com/magazine/india-tech-founders-does-alma-mater-matter 7 Massachusetts Institute of Technology, United States of America. 8 PitchBook Data, Inc. is a SAAS company founded in March, 2007. It delivers data, research, and technology covering the private capital markets. To collect data, it uses machine learning and natural language processing. The company's core product is the PitchBook Platform, a subscription-only database. network of high quality and successful alumni and other social connections, results in greater ease of obtaining startup financing from VCs.
, 2008; Zhu & Zhang, 2010)  in marketing goods and services  (Aral et al. , 2013; Bharadwaj et al. , 2013)  and by serving as a platform for greater consumer engagement and participation  (Chen et al. , 2015; Ghose & Han, 2011; Goes et al. , 2014; Li & Wu, 2018; Miller & Tucker, 2013). In this study, we hand collect information from various social media networks (LinkedIn, Twitter, and Facebook) to determine the strength of the startup's digital network and how it impacts a startup's access to financing. By extending  Crane & Hartwell (2019) , we link signaling theory with human capital theory and social capital theory to test our developed hypotheses. We collected data from 102 startups that obtained funding between the years 2014-2017. To address potential survivorship bias, we included both failed and active startups.
This, in turn, can serve as a positive signal to VCs and increase the likelihood of securing funding. Taken broadly, existing studies suggest  (Goh et al. , 2013; Luo et al. , 2013)  that the use of social media can influence overall firm performance, including the success of early-stage firms and their ability to obtain financing. While these studies mostly use data from established companies, we expect to see a similar logic at work for startup firms. With respect to signaling function, social networking may also serve as legitimization for the startup idea/product or process and, consequently, may facilitate the acquisition of financial capital. Thus, we posit: Hypothesis 5: Startups that have more followers (fans) on social media (Twitter/Facebook/LinkedIn) are more likely to receive more rounds of VC financing.
Data Collection We collected data comprising 47 active startups and 55 failed startups from 2014-2017 through several online sources and random searches (Crunchbase 11 , Traxn 12 , Trak. in 13 , Inc42 14 and Internet 11 Crunchbase is an online database of startups and technological companies around the globe. The database provides information related to funding rounds. We used the paid version, Crunchbase Pro, which allowed access to data pertaining to Indian startups and their investment details. Crunchbase can be used by entrepreneurs, business analysts, researchers, and investors. https://www. crunchbase. com/. 12 Traxn is a database providing information on nearly 10 million companies around the globe. It is a paid database considered to be very accurate and precise. https://tracxn. com/.
13 Trak. in is an Indian blog that list the startup by years. Information, such as the names of startups that have utilized venture capital funding, is available. https://trak. in/Tags/Business/category/startup/. 14 Inc. 42 is a leading Indian media and information platform that is known for its coverage of Indian startup ecosystems. It publishes several reports and articles that offer information on startups that have filed for bankruptcies. This is a very useful platform for accessing information on Indian startups. https://inc42. com/. specified word-based searches 15 ). The Indian VC industry has passed through three distinct phases in the last decade: the growth stage, maturation and moderation, and renewed optimism. We analyzed data from 2014-2017, as these years were marked with heavy investments in Indian startups, and, more specifically, with higher quality investments in digital startups.
We included failed startups to avoid survivorship bias in our sample. T-tests indicated no significant differences between the 102 active startups and failed ones in terms of age, sector, and city. We selected only the technological startups in the domains of Consumer Internet services, Fintech, business analytics, Edtech, Foodtech, Healthtech, Logistics, and Enterprise Applications. We collected information related to total funds raised and multiple rounds of financing through the Crunchbase database, and we cross-checked the results with Traxn and with online media reports  (Trak. in and Inc42)  to check the accuracy of the data. We were also able to obtain information on the names of founders, which we then used on LinkedIn to collect more information related to human capital variables, such as previous experience (number of years of experience and kind of prior experience), educational institution, and gender.
We then collected information related to the strength of the social media network of funded startups through LinkedIn, Facebook, and Twitter and added a combined variable of the total number of fan followers. We also collected information on the region of the startup, the year of launch, and the sector. A detailed analysis of the variables and from where they were collected is provided in the appendix (A1). Data collection ended in July 2018, therefore any data after that date go beyond the scope of our study.
Dependent Variables To test the independent effects of human capital and social networking on access to financing, we use rounds of financing (FIN_ROUNDS) 16 as our dependent variable. Based on the data collected for both active startups and failed startups, we found that only one startup was funded through debt. All of the other startups were funded initially through VC and, later, funded through both VC and private equity. We collected the information on the number of financing rounds starting from seed funding through to the last funding round as of July 2018, for both kinds of firms. We found that while the average round of funding received by successful firms was 3, for failed firms it was only 1. We also collected information on the total amount of financing raised as of July 2018. The average funding amount for the still-active startups was 15 We searched for specific words like "technological startups from India," "emerging startups," "failed startups," and so forth to build our database of 102 startups.
We use random searches so data does not come from one Internet platform. 16 Indian startups can be financed in stages. The process begins from the seed stage, which is the first level of raising capital, solely given on a concept or idea. Early stage investing is identified within Series A and Series B of financing rounds and is associated with the early stage and growth. The next round of financing is associated with expansion. around US$300 million, whereas for failed firms it was $4 million. We needed to consider that in the case of failed startups, the details of the amount raised were absent for 20 startups. Being unable to utilize the amount of funding as the dependent variable, we employed rounds of financing. Only VC and private equity funds are included in our data sample and we do not include funds provided by the government or the founders.
Out of the 214 founders in our data sample, nearly 83% of them attended IIT, IIM, BITS PILANI, or an elite foreign university, and the rest attended other 17 According to our data sample, 73% of the founders came from the Indian Institute of Technology (IITs), the Indian Institute of Management (IIM), the Birla Institute of Technology and Science (BITS) Pilani, or a foreign university like Harvard, Stanford, The Kellogg School of Management at Northwestern University, etc. We refer to reputable rankings that regard these as elite educational institutes. For example, Outlook magazine regards IITs and BITS as the top-ranked Engineering colleges in India: https://www. outlookindia. com/magazine/story/top-100-engineeringcolleges-in-2018/300176. The Indian Institute of Management is the top Business Management school in India: https://www.
Methodology We used SPSS statistical software to test the main empirical hypotheses discussed in the previous sections; this section includes two steps. First, we estimated a regression model to predict the number of financing rounds (FIN_ROUNDS) as a function of the various independent variables and control variables discussed earlier. Post hoc multicollinearity diagnostics indicated variance inflation factors (VIFs) well below the threshold of 5 for all the independent variables tested. However, for rigor and robustness, based on the observed correlation matrix, we also ran structural equation modelling (SEM) with explicit covariance paths for all a priori statistically significant correlations (observed and expected) between the independent variables. This ability of SEM to explicitly handle multicollinearity is one of its important advantages compared to traditional ordinary least square (OLS) multiple regression.
Some signals will be relevant for some stages but become irrelevant in subsequent stages. Thus, it would be interesting to develop a model that tests how different signals help the startup to acquire resources during different stages. Fourth, the study is mainly quantitative and lacks a qualitative view from investors as to what makes a strong signal during the financing rounds. Thus, we encourage future research that can consider integrating both qualitative and quantitative variables so that more understanding can be propounded into the contingencies that enhance the financing of startups. Fifth, the validity and authenticity of the data are limited to what we have collected from multiple databases such as Traxn, Crunchbase, Techinasia, LinkedIn, Twitter, and Facebook. Even though we have sought to verify the information collected, there may still be a chance of some discrepancies, which cannot be avoided.
Participants Participants were recruited as part of an ongoing prospective randomized controlled trial (ClinicalTrials. gov, NCT03049384 and  [26] ). The purpose of the RCT is to investigate the is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted February 25, 2020. ; https://doi. org/10. 1101/2019. 12. 12. 19014829 doi: medRxiv preprint Running Title: PEM in Chronic CRF 5 effect of a tailored versus traditional 12-week exercise intervention on fatigue severity in people with chronic CRF after cancer treatment. Participants were eligible if they were aged 18-75 years and had completed curative-intent cancer treatment (≥3 months and ≤5 years from enrolment), self-identified as feeling fatigued/tired, and self-identified that the fatigue/tiredness developed at any point during the course of the disease (e.