dataesr/scientific-paragraphs-categorization huggingface dataset:
This differentiation in the population structure might also result from a spatial pattern of genetic variation, but with a Mantel test (which relies on a randomization procedure), no significant correlation was observed between the Fst matrix distance and the geographic distance. We alternatively inferred gene flow between the different appellations, using a Bayesian coalescent approach implemented in the Migrate software, and using the method proposed by Sundqvist et al. 53. Both methods indicate asymmetric migrations, and St Emilion as a source of migrants towards Medoc and Pessac Léognan (Supplementary Table  S10 , Supplementary Fig. S7 ). Testing for the impact of the farming system. The AMOVA revealed an impact of the farming system on the population structure (Table  3a ). However, when combining appellation and farming system, neither of the two factors appeared as significant.
Relation between grape and cellar S. cerevisiae diversity. Grapes are one source of the S. cerevisiae strains involved in the winemaking process, but the link between grape and cellar S. cerevisiae populations is still poorly characterized till now. Thus, we aimed to evaluate connections between grape and cellar metapopulations. We collected and genotyped 289 S. cerevisiae strains from 11 spontaneously fermenting musts in 7 cellars. Clusters gathering grape and vat isolates were highlighted in the dendrogram tree (Fig. 1B ). After filtering for clonality and after removing strains related to with commercial yeast starters, this data set decreased to 225 cellar-associated unique S. cerevisiae profiles. In a given appellation, the genetic differentiation between the grape and cellar population is significant with moderate to high Fst values between 0.
cerevisiae. In addition, a low differentiation was observed between the two farming systems which indicates that this factor is not a main driver of the S. cerevisiae population structure in the Bordeaux and Bergerac area. The persistence of commercial S. cerevisiae starters in the vineyard and its impact on autochthonous yeast diversity is another topic that has been investigated by several authors in different wine producing areas. Previous studies reported concordant results indicating an infrequent dissemination of commercial yeast in the vineyard surrounding the winery, and show that the dissemination is restricted to short distances (maximum distance of 100 m around the dissemination area and in a limited period of time)  41, 43. By comparing indigenous S. cerevisiae genotypes with a database of 79 commercial wine strains commonly used by the wine industry, Gayevskiy showed that only a few isolates shared one microsatellite allele with commercial starters, thus supporting the concept that a diverse natural population resides in New Zealand  35.
The 2 multiplex PCR contained (for 8 samples) a total of 15. 5 μl multiplexed primers, 50 µl of QIAGEN Multiplex PCR kit Master Mix and 18. 5 μl water MilliQ. The PCRs were run in a final volume of 12 μl containing 2 μl of cell suspension. The following PCR program was used in the routine: initial denaturation at 95 °C for 15 min followed by 35 cycles of 95 °C for 30 s, 57 °C for 2 min, 72 °C for 1 min and finally a final extension at 60 °C for 30 min. PCR products were sized on a capillary electrophoresis ABI3730 (APPLIED BIOSYSTEMS) using size standard 600LIZ (GENESCAN). Locus YLL049W providing non-reproducible amplification was removed for the subsequent structure and diversity analyses. Capillary electrophoresis runs were read using GENE MARKER (V2. 4. 0,) and the sizes of microsatellites amplicons were recorded to investigate the genetic relationships between strains.
GENCLONE software (V2. 0)  68  was used to remove from our dataset strains with similar profiles resulting from potential clonal expansion. Strains profile comparison with yeast starters was performed using the BIONUMERICS V5. 1 software (APPLIED MATHS, Belgium) with the categorical coefficients associated with the ward algorithm  69  from the microsatellite data size. Strains sharing more than 75% of alleles at 17 loci with commercial strains yeast starters and no missing values were considered as related to these starters. They were retained for spanning tree drawing and removed from the different datasets. These spanning trees were drawn with the BIONUMERICS V5. 1 software. Dendrograms were constructed using Bruvo's distance  50  as proposed by the POPPR 2. 02  70  and neighbor-joining clustering with ape 3.
2  71  under the R environment v3. 5. 2 (R DEVELOPMENT CORE TEAM 2011). The Bruvo distance requires perfect microsatellite loci, which is almost the case for 17 out of 18 loci but not for locus C4 which is composed of two motifs (one locus over 17). The use of this distance provides a phylogenic signal closer to genome sequencing than that observed with Dc Chord distance  9, 72. As a consequence, we have retained this distance, despite potential violation of the initial model proposed by Bruvo et al. 50. In order to assess the robustness of tree nodes, bootstrap resampling was performed by means of R and the pvclust 1. 3-2 package  73  and inferred with MEGA6, all bootstraps lower than 25 were not shown in the trees. Population structure was evaluated using the Bayesian clustering method implemented by the software InStruct that considers the inbreeding is the main sexual mode of reproduction  51  which is the case for yeast.
Five chains of 150,000 iterations with a burn-in of 5000 were run for K = 1 to K = 25. The most likely number of ancestry's populations was selected choosing the lowest DIC (Deviance information criterion). Bar plots presenting the ancestry profile for each population were drawn from the InStruct output file using an R script. On each population, basic statistics were estimated with poppr v2. 8. 3 and the DiveRsity v1. 9. 90 package, and provided as averages between loci. AMOVA pas performed with the Pegas package v0. 12 as proposed by the poppR R package. Pairwise Fst distance and significance tests, and the Mantel test evaluating the correlation between geographic distance and genetic divergence, were performed as implemented with GenAlex v6. 5  [74] [75] [76]. Population geographic differentiation was also performed from the ancestry profile as implemented by the OBSTRUCT V1.
0 software  52. Relative directional migration rates between the five appellations were estimated using the approach proposed by Sundqvist et al. 53  with the website divMigrate (https ://popge n. shiny apps. io/divMi grate -onlin e/), using the D distance  77. Directional migration rates between the four appellations, Medoc (38 genotypes), Pessac Leognan (89 genotypes), Saint-Emilion (7 genotypes) and Entre deux-Mers (5 genotypes), were inferred using Migrate 4. 2. 14, assuming constant population size  78. The dataset included only isolates from the 2012 sampling year, and the 4 loci (YLR, SCAAT6, YKR072c and SCAAT2) which contained several missing data were removed. Identical clones were discarded after GENECLONE analysis, and isolates related to LSA were removed from this dataset. For grape and cellar population comparison, data sets containing a maximum of 20 individuals were built by random sampling using a custom R script, from the data of the 4 cellars (or grapes) from Pessac Leognan, Bergerac and Saint Emilion in order to avoid unbalanced sampling between cellars and between regions.
Fst were estimated from the sampled population using a custom R script and Fst calculated using the Hierfstat v0. 04-22 package under the R environment. This randomized dataset was then further used for the estimation of exchanges between cellars and grapes using Migrate 4. 2. 14. Given the small proportion of missing data, the 17 loci were used for the analysis. All these datasets are available on the Open Data portal of INRAE (https :// doi. org/10. 15454 /GMRGP O).
This type of analysis is presented in a paper related to ours,  (Decarolis, Goldmanis and Penta, 2018). 27 Competitive bidding with quality scores has also been studied by  Athey and Nekipelov (2014) , who introduced introducing uncertainty over quality scores. Such an extension is pursued in  (Decarolis, Goldmanis and Penta, 2018)  who develop a criterion to detect various forms of collusion based on the variables which are typically contained in the datasets available to the auction platforms.
and P. Strobel for their help in the growth of FeSe crystals as well as A. Boothroyd, V. Brouet, L. Fanfarillo, R. Fernandes, Y. Gallais, P. J. Hirschfeld, D. LeBoeuf, I. Mazin, C. Meingast, I. Paul, J. Schmalian, Y. Sidis, I. Vinograd, S. Wu, T. Wu, and particularly Igor Mazin, for discussions. Work in Grenoble was supported the Laboratoire d'excellence LANEF in Grenoble (ANR-10-LABX-51-01). Part of this work was performed at the LNCMI, a member of the European Magnetic Field Laboratory (EMFL). DDS and BMA acknowledge financial support from the Carlsberg Foundation.
Charting the data Following the recommendation of  Colquhoun et al. (2014) , the two researchers created a data extraction grid in Microsoft Excel. The sheet contained 18 columns that may be divided into two categories: general information about publications and data on the application of the TPB (see Table  2 ). Initially, the two researchers independently extracted the data from 10 articles. The results of this initial extraction were compared, and the coders discussed the few differences. To increase the reliability of the codification process, the explicit definition of each item was added to the extraction grid. Then, the researchers extracted the data from another 10 articles, and the obtained results were identical for both coders. The remaining articles were divided equally between them.
We theorize that under conditions of higher uncertainty these tendencies will be prominent, but over time as uncertainty diminishes these 1 Individuals can reciprocate advice through other means. Our focus is on advice seeking so we take a very narrow view of reciprocation and only theorize reciprocity in the context of the flow of advice within a dyad. tendencies will change with the structural tendencies diminishing and the tendency for the acquired firm to make ties also reducing. To test our hypotheses we use data over four time periods in a recruitment consulting firm that had undergone an acquisition. Transferring complementary market and productbased knowledge between legacy firms was one important rational underlying the acquisition. As this knowledge is highly tacit in both firms, advice seeking from other individuals constitutes the main means through which knowledge can be accessed and shared.
Sample Our study uses network data from a longitudinal single case study of an acquisition in the recruitment consultancy sector. The sample comprises a total of 42 consultants (30 of the acquiring firm and 12 of the acquired firm). Network data was collected through a network survey following the acquisition event and then every six months after the acquisition to all consultants of both legacy firms. In total we analyze four observation periods, covering the two-year period following the acquisition. Response rates vary from 91% to 100% according to the observation period. In addition, interviews were conducted at the time of the acquisition and one year post-acquisition with a sample of the consultants. As part of the interviews the consultants were asked about work practices, their work and support relations, as well as the change induced by the acquisition in their daily work, position within the firm and relationships to their colleagues, as well as whether they was any personal resentment about the situation.
Measures Advice network. The focus of our analysis is the changing network of advice ties. In our survey we measured advice seeking by asking respondents to indicate the people that they had sought advice from during the previous six months. Answer options for the advice seeking questions were 1 = sporadic one-time advice seeking, 2 = periodic advice seeking and 3 = regular advice seeking. Since we are interested in understanding advice seeking relationships that occur on at least a semi-regular basis, we chose to dichotomize the data at responses of two and above, i. e. , periodic or regular advice seeking. For a discussion and analysis of the other two ways in which the data could be dichotomized see Appendix A.
Method of analysis We examine the micro-processes of network change at four points in time after an acquisition event. To analyze our data we need a modeling framework that takes into account the dependencies between the cases and the changes in the network over time. To do this we use an actor-orientated Simulation Investigation for Empirical Network Analysis (SIENA) modeling framework in the RSiena software package  (Ripley et al. , 2019). The SIENA framework is a stochastic actor-orientated model which assesses the probability of different types of network change. The model can account for network structural changes such as reciprocity, and changes concerning individual attributes such as age  (Snijders et al. , 2010). The model has certain assumptions such as changes in network ties being continuous and that these changes are based upon a Markov process  (Snijders et al.
The objective function for the network is as follows: ƒi(β,x) = Σ βk Ski (x) k In the equation, ƒi (β,x) is the objective function with i being the focal actor and x the network. The function Ski (x) is the effects on the network from the perspective of i. These effects include the tendency for reciprocity or transitivity as well as the tendency of people with a certain individual attribute, such as gender or age, to change their network ties in a certain way. In the model, βk is the weight of the change. The significance of a parameter is calculated by comparing the t-ratio, i. e. , the estimated parameter divided by standard error, with a standard normal distribution. Missing data in SIENA models. We address missing data in two ways. First, individuals are coded as structural zeros if they are not present in the dataset during a specific time period, for example, if they have not yet joined the firm or if they have left the firm.
Limitations and future research In our research we were able to gather four waves of data which allowed us to develop valuable insights into the micro-processes of change in the advice seeking network after an acquisition event. However, we only examine one acquisition and replication of our findings would increase the robustness of our results. In addition, to avoid survey fatigue we were not able to gather data on individual traits. One avenue for future research would be to examine if advice seeking micro-processes differed based upon personality traits such as the Big Five personality factors or self-monitoring behavior  (Fang, Landis, Zhang, Anderson, Shaw, & Kilduff, 2015). Our focus has been on advice seeking as we believe this was the most pertinent network to examine in the context of a recruitment consulting firm.
Sample and data sources To construct our data set, we use information from various sources. Data on credit ratings and educational characteristics are obtained from Capital IQ. For firm credit ratings, we use the Standard and Poor's (S&P) long-term issuer credit ratings. These ratings range from D/SD to AAA, and they reflect the S&P's assessment of an issuer's creditworthiness to respect its senior debt obligations. We collect information on the types of degrees held (Bachelor, Master, MBA, PhD), the major discipline of study (Business-Economics, Engineering or other) and the perceived quality of the educational institutions (Ivy League and THE-listed Universities) for each board member of interest. Data on firm-specific characteristics are also collected from Capital IQ, whereas data on the macroeconomic fundamentals and sovereign ratings are obtained from the World Bank, IMF and S&P respectively.
Our sample includes 1,618 firms operating in 39 countries. 7 As it stands at the time of writing the paper, Capital IQ provides information for the current board composition 8 , absent, however, of the year in which each member joined the board. Thus, our analysis is restricted accordingly to the coverage of two points in time in which we collected data for our analysis, those being March 2018 and March 2019. The credit ratings that were available at these points in time correspond to end of 2017 and end of 2018, respectively. In that case, information for the financial statements and the board characteristics correspond to the preceding fiscal year  (i. e. 2016 and 2017, respectively). We exclude financial firms, with the remaining ones operating in various sectors, namely: (i) energy, (ii) health, (iii) industrials, (iv) information technology, (v) staples, (vi) telecommunications, and (vii) utilities.
12%. 8 When it comes to the education of the board, we only collected data for firms that had available information for all three key board members (two in the case that the CEO has also a chairing role). There were few cases where, for a given individual, only the highest degree was reported, e. g. a Master's or an MBA, and not the prior qualifications succeeded before obtaining that degree. For those individuals, we assume that they are also in possession of a Bachelor's degree. [Insert Table  1  about here].
These are further discussed in Section 4. 1. Finally, we include several macroeconomic variables to account for potential forms of heterogeneity across the countries in which the firms operate. We therefore include: (i) GDP growth (%); (ii) inflation (%); and (iii) sovereign S&P ratings. The data sources and all the variables are described in Table  A. 1 (see Appendix). Table  A. 2 further presents the correlation matrices for all variables.
Acknowledgement This action is being led by the French Ministry for Agriculture and Food and the French Ministry for an Ecological and Solidary Transition, with the financial support of the French Biodiversity Agency on "Resistance and Pesticides" research call, with the fees for diffuse pollution coming from the Ecophyto Plan. European Regional Development Fund (FEDER) and French Water Office of Martinique also supported this action with the RIVAGE project (MQ0003772-CIRAD). We are grateful to the farmers of the Galion River watershed and to the supervisors of the agricultural and environmental organizations we interviewed. We also thanks anonymous reviewers for helpful comments.
Sample The data used in this study were collected in the first quarter of 2015 to the fourth quarter of 2017 with information on 1,213 foreign manufacturing subsidiaries located in 44 countries in Asia, North America, Central and South America, Europe, Oceania, Middle East, and Africa. These subsidiaries belong to 191 MNEs, 65 of which are large firms affiliated to 30 Korean business groups (i. e. , Chaebols), 114 are independent Korean SMEs not affiliated to business groups, and 12 are classified as other organizational types, e. g. , big companies not affiliated to Korean Chaebols, etc. We conceptualized dual knowledge flows between a focal subsidiary and its MNE headquarters using pre-specified criteria based on prior literature. In fact, our sampling approach and that of  Dellestrand and Kappen (2011, 2012)  are alike.
Our researchers' meeting with respondents enhanced the reliability and validity of the data. We matched our interview data with multiple secondary data sources to allow triangulation and also collect additional information on the Korean MNEs and their foreign subsidiaries. We collected secondary data from multiple sources, including financial and accounting information on Korean MNEs from the KISLINE and KISVALUE databases, Korean outward FDI information from the Korean Ministry of Strategy and Finance (KMSF) database, and the Korea Listed Companies Association (KLCA). We also compiled data from the archives of each firm's annual reports from the Korea Information Service as well as the DART (Data Analysis, Retrieval, and Transfer System) of the Korean Financial Supervisory Service. In addition, comparisons of firm age and firm size between the responding and nonresponding firms revealed no significant differences; nor did comparisons between early and late respondents.
, 1985; Ma and Christensen, 2020; MacCarthaigh, 2012; Nakro sis and Budraitis, 2012; Nethercote, 2000; Rolland and Roness, 2011, 2012; Sarapuu, 2012; Wettenhall, 1976). Drawing on databases of diverse organizational units from ministries to agencies, scholars have shown the proliferation of organizational forms and their growth over time as compared to the traditional structures of ministerial departments. In Ireland, the number of ministerial departments remained relatively constant from 1923 to 2010. For both Estonia and Lithuania, an initially large number of ministerial departments decreased soon after independence as a result of mergers and reorganizations. In Norway, Rolland and Roness (2011) note the existence of ministerial units and their subunits within their database but focus on independent organizations falling outside of ministries.
Population of organizational units within public bureaucracies A framework for understanding stability and change in organizational populations must account for their most basic characteristic: size. The size of a population, however, masks obvious sources of instability. Hence, a framework for understanding stability and change in organizational populations must discern the different types of events that indicate transitions in addition to those that lead to population size change. Such a framework will also record the event histories of all members of the population, allowing for both population mapping and event history analysis. Like previous mapping projects such as the Norwegian State Administration Database, our approach covers all observed events. However, it is not limited to population snapshots taken at the start and end of the observation period (cf.
Further research This novel framework for describing structural changes in hierarchical organizations helps to bridge the gap between comprehensive mapping projects and event history analyses. The design allows for cross-national comparisons of the central state bureaucracies of France, Germany and the Netherlands from 1980 to 2013. To conclude, we discuss some promising avenues for future research that the framework may facilitate. First, our framework can be linked to other mapping projects. For instance, the SOG-PRO data can be linked to existing data sets on structural changes in bureaucracies, allowing broader cross-national comparisons. Our approach can also be applied to map other hierarchical organizations over time, including subnational administrations, supranational bureaucracies like the European Commission and the administrations of international organizations.
By mapping a large variety of such organizations in a similar way, it will be possible to detect common trends in very different contexts and to explain cross-national and cross-organizational differences in structural change beyond these three country cases. Second, mapping organizational change in this way can be used to analyse how reforms like NPM or post-NPM, political factors such as partisan ideology  (Fleischer et al. , 2018) , or policy agendas influence the formal structure of central bureaucracies. By linking organizational change data to existing cross-national data sets collected over long time periods -such as the Parliaments and Governments Database (ParlGov) and the Comparative Agendas Project (CAP) -the framework can provide new insights into the determinants of structural change from a variety of political, administrative and external sources.