dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Source data are provided with this paper. All species richness data necessary to reproduce the results of this manuscript, including those retrieved through the literature search, and all raster files (format: GeoTiff) used to create the multi-grain maps of species richness are available at: https://doi. org/10. 25829/idiv. 3506-p4c0mo (ref. 214 ). The vegetation-plot raw data contained in the sPlot database are available upon request by submitting a project proposal to sPlot's Steering Committee. The proposals should follow the Governance and Data Property Rules of the sPlot Working Group available on the sPlot website (www. idiv. de/splot). Source data are provided with this paper.
5% and 13% of the total dataset. A batch size of 1 × 10 5 was chosen for the following experiment. We note that small batch sizes bring a degree of instability to the loss, as can be seen in the line representing a batch size of 5 × 10 4 in fig. 2. It was found that batches smaller than those shown in Fig. 2  led to increasing loss values over the training, with a batch size of 1 × 10 4 leading to training regularly failing with the Punzi-loss increasing and plateauing at a value above the initial loss. This can be understood as a result of the limited number of signal events present in any given batch of small size, leading to large statistical fluctuations in the calculated loss values. This lower limit is, of course, study dependent. Similarly, we note large instability in the mono-batched case.
The simulation and reconstruction of the events were done using GEANT4  [12] , and the Belle II Analysis Software Framework  [13]. The analysis is carried out via the search for a peak in the distribution of the squared mass recoiling against the two-muon system. An excess of entries beyond that of the expected background at a given mass would indicate the presence of such a Z' particle of that mass. This distribution is divided into (potentially overlapping) bins with bin widths corresponding to ±2σ of the fitted Z' signal distributions. During both the initial BCE and subsequent Punzi-loss training, only every second generated Z' mass was used. For the calculation of σ min in Eq. 12 only signal and background variable description p * t,thrust (µ) The transverse momentum component of the muons with respect to the thrust axis.
Acknowledgements The authors would like to thank the  Belle II Collaboration and Belle II software group  for useful discussions and suggestions on how to improve this work. P. Feichtinger ,  H. Haigh  and  G. Inguglia  would like to acknowledge funding received under the Horizon 2020 framework of the  European Research Council ,  ERC StG Nr. 947006   InterLeptons , and under the FWF standalone framework with grant Nr. P31361   Searches for dark matter and dark forces at Belle II. J. Kahn 's work is supported by the  Helmholtz Association Initiative and Networking Fund  under the  Helmholtz AI  platform grant. F. Meier work is supported by the  US Department of Energy.
A. Gompers, 1995; Sapienza, 1992; Wadhwa and Kotha, 2006). Although prior research assumes that all incumbent firms are equally likely to engage in misappropriation when there are incentives to do so  (Diestre and Rajagopalan, 2012) , our study unpacks this assumption and finds that incumbent firms differ in their opportunistic propensities, and that new ventures vary in their abilities to distinguish incumbents of questionable character. As such, our study responds to the call for more research on the heterogeneity of VC investors  (Dushnitsky and Shaver, 2009)  and contributes to research on business similarity or relatedness and geographic proximity between new ventures and incumbent firms more broadly. The next sections present the literature review and the development of hypotheses. Next, we present the methodology and dataset in addition to the estimation approach used to test our hypotheses empirically.
This kind of sample enables us to examine the marginal impact of CVC funding compared to only IVC funding. We obtain complete investment information from VentureXpert, an extensive database on venture investments that has been broadly applied in prior studies  (Titus and Anderson, 2018; Wadhwa et al. , 2016). To obtain the necessary financial and accounting information, we use Standard and Poor's Compustat database. The United States Patent and Trademark Office (USPTO) database is used to obtain patent application information. To capture the time of knowledge creation, we use the application date of the granted patent. Finally, Bloomberg, our fourth database, is used to find the ticker symbols, which help us obtain data from Compustat because of the differences in the names of companies in the databases.
To combine the data from the Thomson One, USPTO, and Compustat databases, the names of the companies were hand-matched in all the databases. We choose 1998 as the start of our study period because it corresponds to the increase in CVC investment among firms and companies, and we end the sample at 2013 because of the lag between the patent application date and its granting. We construct a sample of corporate investors as reported by the Thomson VentureXpert database. The Standard Industrial Classification codes (SIC) data of the firms are collected from Standard and Poor's Compustat and VentureXpert databases. We obtain our final sample at the company level. A brief description of the variables used in running the estimations and building the models is presented in the Appendix.
Independent variables Dummy CVC. The primary independent variable of concern measures whether a company obtains funding from corporate investors or IVC. To determine the fund type, we obtain the list of investor fund types provided by the VentureXpert database. Applying the keywords on the kind of investor from the Thomson One database, we have 652 corporate investors in our combined sample. In line with  Chemmanur et al. (2014) , we construct the dummy variable CVC to take the value of one if a company has at least one corporate investor and zero if it is funded solely by IVC. Patent stock. According to  Cohen and Levinthal (1990) , the patent stock of a company is employed as an index of the company's absorptive capacity. It is measured by estimating the depreciated sum of all patents for which a company has applied at time (t) at a depreciating rate λ of 30%  (Blundell et al.
, 2014). It is an indicator of mature companies  (Guo et al. , 2015). Investment duration. This variable represents the years that capture the length of investment at the time when we close the window of observation. The investment duration is the difference in days when the investment was received and the end of our consideration. Investment amount. This variable is the dollar amount invested up to the close of our observation window. The investment or equity amount is the disclosed amount of the equity share of the investment round invested in the company at the round date. It is measured by adding the disclosed amounts of equity across all rounds in millions of dollars. Thomson One database provides the estimated and disclosed equity amount per round  (Guo et al. , 2015). CVC Annual Availability.
Estimation approach We examine the innovation production of a company as a function of venture capitalists. As investors choose which companies to fund, our empirical approach considers nurturing and selection results. Our dependent variables are Patents i,t and Citations i,t , which are count data related to the number of patent applications and citations of a company per year. We follow the literature (Alvarez-Garrido and  Dushnitsky, 2016; Wadhwa et al. , 2016 ) by using a negative binomial model, as our data take only integer, nonnegative values and exhibit significant overdispersion. The nonlinear model applied avoids heteroskedasticity, which is generalized from a Poisson model and allows for overdispersion by combining an individual, unobserved effect with the conditional mean. Since the mean and variance of the patent data are not proportional, we cannot apply a Poisson model, which cannot be used when the standard deviation of the dependent variable exceeds its mean value  (Hausman et al.
Determinants of CVC Funding-First Stage regression In the first-stage regression, we attempt to determine the match between companies and CVC or IVC funds using probit regression. The instrument, CVC annual availability, is positive and significant. We follow  Dushnitsky & Shaver (2009) 's model to predict the likelihood of CVC funding. The dummy CVC is the dependent variable of the first stage. The additional variables should capture the company features that influence VC funding. Many circumstances determine the investor's decision on whether to fund a company, and the company is not randomly matched with venture capitalists. Many company characteristics determine whether there will be CVC funding. Referring to the available information in the Thomson One database, we retrieve a regression that reports the results of some explanatory indicators for the companies that receive VC financing.
Sun et al. (2021)  [9]  proposed a new corrugated structure with a spiral-form, so-called SpiroPak, and compared its performance experimentally and numerically with a conventional corrugated structured packing (Mel-lapak®-250Y). Neukäufer et al. (2019)   [10]  presented new crystal-inspired forms and made a numerical parametric study to compare the pressure drop and liquid holdup performances of these structures. The packing performance is generally evaluated experimentally. However, the combination of computer-aided design (CAD) software and AM could lead to the conception of many possibilities of a new G/L contactor that would require too many resources and time to produce and test experimentally. Effectively, the scale of the experimental setup should be sufficiently large to be able to extrapolate the results and use them in the design of a potential industrial separation unit, which has a column diameter ranging from 0.
Experimental setup The pressure drop evaluation is carried out using an experimental rig that comprises a glass column filled with a packing. The flow diagram of the experimental setup is given in Fig. 1. The glass column of 150 mm diameter, indicated by (4) in the figure, accepts a one-meter-high packing segment. Such a column size for packing testing has been justified by  Kawas et al. (2021)   [16]. Pressure sensors are positioned on the two flanges in the upper and lower extremities of the packing  (5). A Keller-Series-30 differential pressure transmitter with a digital pressure indicator (EV-120) measures the pressure drop, providing a high precision measurement chain of ±0. 01%. The pressure drop is recorded using the acquisition software Control Center Series 30 (CCS30). Two types of experiment could be performed using this pilot plant: gas flow only or G/L countercurrent flow that correspond to dry or irrigated pressure drop, respectively.
A tube linking the liquid evacuation to the top of the column is used to equalize the pressure and help liquid evacuation. A small air stream is sent to both pressure sensors to avoid the entry of liquid, which falsifies the measued pressure value. The low airflow rates are regulated by two flowmeters  (10). Fig. 1  illustrates the positions of the valves for the G/L countercurrent experiment, black = closed. In the present study, only the dry pressure drop is of interest. This is measured for twenty-one air flowrate values corresponding to different gas velocities ranging between 0-3 m. s -1. The software records the pressure drop every second for three minutes for each flow rate value. A Matlab code processes this data by calculating local averages and removing noise and aberrant points using a Student t-test.
According to the experimental data produced using the setup described in paragraph 2, they generate less pressure drop and have a higher flooding point, F-factor > 2. 4 Pa 0. 5  [16]. Table  1  reports the CAD geometric parameters of the three TS structures and the dimensions of their respective EUs, where ε and A s are calculated using the CAD software. [25, 26]  The Kelvin cell is a three-dimensional regular geometric form, see Fig. 4(a). One of the following three parameters is sufficient to define the regular KC:.
As for the KC_12H foam packing, the real average porosity of the produced blocks corresponds to that calculated by the CAD software, i. e. , 86%. Thus, the pressure drop of KC_12H packing only was studied numerically and compared with its experimental value. This latter corresponds to the pressure drop of 145 mm wide and one meter high KC_12H packing. Despite the correspondence in experimental and CAD porosity for this packing, the strand thickness is overestimated for horizontal strands and underestimated for vertical ones, i. e. , vertical strands are thinner than expected or missing, and other strands are thicker than expected.
DQPB is software for calculating U-Pb ages while accounting for the effects of radioactive disequilibrium among intermediate nuclides of the U-series decay chains. The software is written in Python and distributed both as a pure Python package, and a stand-alone GUI application that integrates with standard Microsoft Excel spreadsheets. The software implements disequilibrium U-Pb equations to compute ages using various approaches, including concordia-intercept ages on a Tera-Wasserburg diagram, disequilibrium U-Pb isochron ages, Pb/U ages based on single analyses, and modified 207 Pb ages. These age calculation approaches are tailored toward young materials that cannot reasonably be assumed to have attained radioactive equilibrium at the time of analysis, although they may also be applied to older materials where disequilibrium is no longer analytically resolvable.
1 ). Because these more complete disequilibrium U-Pb equations are rather cumbersome to work with, they are most conveniently implemented using specialised software or in-house computer code. Various approaches have been devised to achieve this. Isoplot  (Ludwig, 2012)  is probably still the most widely used software within the geochronology community and con-  tains in-built functions based on  Ludwig (1977)  that may be used to calculate disequilibrium U-Pb ages as part of a spreadsheet-60 based approach. However, this has a number of limitations. Firstly, Isoplot, which is distributed as an Excel add-in, is no longer being maintained and is incompatible with recent versions of Excel. Secondly, the Isoplot code is protected and hence not readily modified or extended, for example, to produce plots of disequilibrium U-Pb age data.
Thirdly, numerical computing and plotting within the Excel environment is relatively limited. More recently, other software packages for handling disequilibrium U-Pb age data have been developed  (Engel et al. , 2019) , or are in the developmental stage (additions to the Isoplot-R package of  Vermeesch, 2018) , although these solutions are either not yet documented or difficult to implement as part of a stand-alone workflow for those with little computer programming experience. Here we introduce DQPB, a software package for calculating disequilibrium U-Pb ages. DQPB implements the equations of  Ludwig (1977)  to calculate ages using approaches that are suited to a variety of young sample types. The following sections outline software functionality and discuss approaches implemented for age calculation, error propagation, linear regression, weighted average calculations, and plotting.
2 Software overview DQPB is written in Python, an interpreted, high-level, general-purpose programming language that is rapidly gaining popularity within the geosciences. DQPB is available both as a regular Python package and a stand-alone application that does not require users to have a separate Python distribution pre-installed (see Sect. 9 for further details). Python offers several advantages as a language for scientific software development, including its open-source status, well-equipped libraries of functions and routines for scientific computing, and relatively easy-to-read syntax (e. g. ,  Oliphant, 2007). Being a general-purpose language, Python also offers significant advantages in developing stand-alone graphical user interface (GUI) based applications, when compared to "domain specific" scientific languages such as MATLAB and R.
DQPB is built on the core Python scientific computing libraries NumPy  (Harris et al. , 2020) , SciPy  (Virtanen et al. , 2020)  and Matplotlib  (Hunter, 2007). It also takes advantage of PyQt to provide a modern GUI on macOS and Windows, and xlwings to facilitate integration with Microsoft Excel. This allows users to select data from an open Excel spreadsheet, perform calculations via the graphical interface, and have results (both numeric and figures) output to the same spreadsheet once computations are complete. In this way, it emulates the ease of use of the popular Isoplot program  (Ludwig, 2012). As is common practice with open-source software, all Python source code is available for viewing, download, and modification, via an online code repository (see Sect. 9).
Disequilibrium U-Pb age calculations DQPB employs an modified version of the equations of  Ludwig (1977)  to calculate U-Pb ages and plot disequilibrium age data. These equations were initially derived by Ludwig from a form of Bateman's 1910 solution which assumes zero initial abundance of all intermediate daughter nuclides, and independently considers in-growth of Pb* from decay of the primordial parent and each preceding intermediate nuclide (see also  Ivanovich and Harmon, 1992; Neymark et al. , 2000). These separate components are then summed, or "superposed"  (Bateman, 1910) , to obtain the total quantity of Pb* as a function of time (t). Following this approach for the 238 U decay chain, and ignoring intermediate nuclides with a half-life less than or equal to that of 210 Pb (i. e. , ∼ 22 a), results in an equation of the form: F = F 1 + F 2 + F 3 + F 4 , (1) where F = 206 Pb * / 238 U, and each term represents in-growth from the primordial parent (subscript 1) and each preceding intermediate daughter nuclide in the decay chain (subscripts >1).
These correlations can be non-trivial and should be considered in any weighted average calculation, or alternatively propagated by quadratic addition after taking the weighted average (second approach only) -see  Ickert et al. (2015). DQPB allows users to compute disequilibrium 206 Pb/ 238 U, with low detrital content, it is also possible to reference to 208 Pb under the assumption that 232 Th has produced negligible radiogenic 208 Pb since the time of system closure  (Getty et al. , 2001). The two formulations are mathematically equivalent, but the latter can be advantageous where accurate measurement of 204 Pb proves difficult, such as in ICP-MS dating of young samples  (Engel et al. , 2019). While U-Pb isochron approaches can be less reliable than concordia-intercept ages, especially for young data sets incorporating the low abundance 204 Pb isotope, they are offered in DQPB because of their potential utility in computing ages for Pb-rich materials where the disequilibrium state of only one of the U-series decay chains is well constrained.
Regression protocols Linear regression algorithms capable of accounting for analytical uncertainties and accommodating the possibility of "excess scatter" (i. e. , scatter in excess of that due to assigned analytical uncertainties) are crucial to attaining reliable U-Pb isochron and concordia-intercept ages. DQPB offers two different approaches to regression fitting. The first is rooted in a classical statistics paradigm and emulates the default regression fitting protocols of Isoplot  (Ludwig, 2012). The second approach takes advantage of recent developments in the application of robust statistics to isochron fitting, implementing the spine algorithm of  Powell et al. , (2020) , as well as a newly developed robust regression algorithm. For the classical statistics-based approach, linear regression of the data is first performed using the error-weighted leastsquares algorithm of  York et al.
, 2002) , or equivalently, the "probability-of-fit" value  (Ludwig, 2012) , the initial York fit and analytical uncertainties are retained. However, if the mswd value falls outside such limits, the dataset is deemed likely to contain a component of excess scatter, which may be either "geological scatter" (variability in initial Pb composition, open-system behaviour etc. ) or some unaccounted-for component of analytical uncertainty. Provided the mswd is not unreasonably high, analytical uncertainties likely still dominate the uncertainty budget, and, on this basis, the York fit parameters are retained but data point uncertainties are expanded so as to reduce the re-computed mswd to 1 (i. e. , the "model 1x" borrowing the terminology of  Powell et al. , 2020). On the other hand, where mswd lies well outside a probabilistically acceptable range, the assumptions of the York fit are clearly violated, and it is commonplace to turn to other classical statistics based approaches, for example, by employing the Isoplot model 2 or model 3 fits  (Ludwig, 2012).
The model 2 fit, on the other hand, makes few assumptions regarding the statistical distribution of the excess scatter, however, it weights all data points equally and does not account for analytical uncertainties at all. In an effort to address these limitations, DQPB implements the robust spine algorithm of  Powell et al. (2020)  as the default regression fitting algorithm for all data sets. The spine algorithm exhibits a number of favourable properties that arguably make it more generally applicable to U-Pb datasets compared to the classical statistics approach outlined above, and improves on previous robust approaches to isochron fitting (e. g. ,  Rock and Duffy, 1986 ) by accounting for assigned analytical uncertainties. The spine algorithm minimises a piece-wise objective function (the "Huber loss function"), whereby data points lying along a central linear band (i.
s is the median absolute deviation (nmad) of weighted residuals, normalised to be equal to the standard deviation for a strictly Gaussian distribution. This statistic tends toward 1 for well-behaved data sets and may be employed in a similar fashion to the mswd, although, in contrast to mswd, confidence limits on spine width must be derived from simulation rather than from a formal statistical distribution  (Powell et al. , 2020). DQPB outputs s along with this simulated upper 95% confidence bound, allowing users to assess if the central spine of data is sufficiently well-defined for accurate use of this algorithm. A second robust regression approach is also offered for datasets that have an s value exceeding this upper confidence limit, but are still believed to contain age significance (see Appendix C).
Where the mswd value clearly lies outside a probabilistically acceptable range, but the data are still believed to constitute a single geologically meaningful age population, it is commonplace to either manually reject one or more "outliers" and recompute the weighted mean, or alternatively parameterise this additional scatter as an external Gaussian-distributed component of uncertainty (e. g. ,  Ludwig, 2012). Because this approach is subject to many of the same limitations as the classical statistics-based regression fitting approach outlined above, DQPB offers an alternative algorithm for computing weighted average ages based on robust statistics. Robust statistics approaches of varying complexity have previously been adopted in geochronology, ranging from use of the simple median along with confidence intervals based on the Wilcoxon signed-rank test  (Rock et al.