dataesr/scientific-paragraphs-categorization huggingface dataset:
From the resulting optimal UB matrix, strain can be computed, given some reference lattice parameters that define a relaxed unit cell. The minimization of (  8 ) was performed using a leastsquares algorithm provided in the Python library SciPy  (Jones et al. , 2001). The implementation was based on the ImageD11 software and can be found at (https://github. com/FABLE-3DXRD/S3DXRD). The full strain tensor field is retrieved by repeating the single-crystal refinement procedure for all points on a uniform grid with spacing equal to the beam width. As pointed out by  Hayashi et al. (2015) , the best possible spatial resolution of this approach is limited by double the beam width. This is apparent by considering that equation (  4 ) is fulfilled as long as any part of the beam intersects P.
Relevant experimental parameters can be found in Table  1. Preprocessing of the diffraction data was performed primarily using the FABLE software suite  (Sørensen et al. , 2012). The grain shapes were deduced using filtered back projection as discussed in Section 3. Implementation of the back projection is available at https://github. com/FABLE-3DXRD/S3DXRD, together with implementations of the three reconstruction algorithms. Owing to time constraints, the experiment was performed with a step size of 0. 5 mm in z, which is to be compared with the beam size of 0. 25 mm. Linear interpolation between reconstructed slices has thus been performed in the presentation of 3D strain fields. Further specifics regarding the sample preparation, background of the experiment and diffraction data preprocessing are given by  Hektor et al.
(2019). As strain is a measure of relative displacement, a reference configuration must be selected. Here we have used the lattice parameters of Table  2  to define a relaxed Sn unit cell. These  parameters represent the sample average lattice parameters, calibrated during grain indexing using the ImageD11 software. All strain fields presented in this paper are given in the ! coordinate system. In ASR, the constraint imposed on the strain difference, ÁE ij , between two neighbouring voxels was taken as jÁE ij j < 5 Â 10 À4 : ð29Þ The resulting reconstructions of the selected grain are presented in Fig. 6. The agreement between the reconstructions provides important information on the accuracy of the methods. A set of residual fields are introduced to illustrate this. These are defined as the difference in reconstructed strain fields between the three methods.
(2015) ;  Beuther et al. (2016) ;  Wang et al. (2020a). We also used the GALFA-Hi observations described in  Peek et al. (2018)  to establish a comparison with the highest resolution single-dish Hi observations. GALFA-Hi has a 4 FHWM angular resolution. At this resolution, the RMS noise of GALFA-Hi and THOR-Hi per 1. 5 km s -1 velocity channel is 126 and 358 mK, respectively. We re-projected the GALFA-Hi into the THOR-Hi spatial and spectral grid in two steps. First, we smoothed and re-gridded the data in the spectral dimension by using the tools in the spectral-cube package (http://spectral-cube. readthedocs. io) in astropy (Astropy  Collaboration et al. 2018). Second, we projected the observations into the same spatial grid of the THOR-Hi data by using the reproject package (http://reproject.
Characterization of the filament orientation Once the filamentary structures are selected, we use the angles estimated using Eq. 3 to study their orientation with respect to the Galactic plane, as illustrated in Fig. 1. The histograms presented in Fig. 2  show the variation of the preferential orientation across velocity channels. For a systematic evaluation of this variation, we use three quantities commonly used in the field of circular statistics (see for example, Batschelet 1981): the mean resultant vector (r), the projected Rayleigh statistic (V), and the mean orientation angle θ , which are calculated using the functions in the magnetar package (https: //github. com/solerjuan/magnetar). These statistical tools provide different and complementary information on the distribution of orientation angles, for example, r indicates if the distribution is flat or there is a preferred orientation.
Comparison to previous observations We compared the results of our study with the analysis of the GALFA-Hi observations, which do not cover the entire area of the THOR survey but provide a benchmark dataset to test the possible artifacts introduced by the side lobe noise in the interferometric observations. Figure  5  presents an example of the Hessian analysis applied to the same region and velocity channels in GALFA-Hi and THOR-Hi. Visual inspection of these and other regions confirms that the structures selected in THOR-Hi have a counterpart in GALFA-Hi. In general, structures that appear monolithic in GALFA-Hi are decomposed into smaller components, and filamentary structures appear to be narrower in THOR-Hi. Moreover, the general orientation of the structures is very similar, further confirming that the filamentary structures in THOR-Hi are not produced by the side lobe noise.
The direct comparison of the V values in both datasets, shown in Fig. 6 , provides a quantitative evaluation of the differences in the orientation of the filaments in GALFA-Hi and THOR-Hi. It is evident that there is a linear correlation between the values of V in both datasets, as expected from the common scales in the observations. This trend is offset from the direct one-to-one relation, for example, the tiles that present V ≈ 2 in GALFA-Hi show V ≈ 4 in THOR-Hi. This increase in the values of V is related to the higher number of independent gradients that is obtained with the higher resolution THOR-Hi observations, as detailed in App. B. The factor of two is consistent with the square root of the ratio between the statistical weights used for each dataset, which following Eq. (  5 ) is equivalent to the ratio between the spatial scales at which we evaluated the filamentary structures, namely, the kernel size of 120 , and the GALFA-Hi 240 beam.
The positive values of V in Fig. 6  indicate that the general trend across l and v LSR is roughly the same in both datasets, that is, filamentary structures in the Hi emission run mostly parallel to the Galactic plane. Unfortunately, the region of the lv-diagram with the most prominent grouping of V < 0 in THOR-Hi, l ≈ 27 • and v LSR ≈ 100 km s -1 , is not covered by the GALFA-Hi observations. However, the general agreement obtained in the region covered by both surveys indicates that the global results of the Hessian analysis of THOR-Hi are not the product of potential artifacts in the interferometric image reconstruction. We provide further details on the comparison between the GALFA-Hi and THOR-Hi in App. B.
Initial conditions The FRIGG simulations used the RAMSES code and take place in a stratified 1-kpc side box with SN explosions and MHD turbulence. This is a standard configuration that can be found in other works, such as, de Avillez & Breitschwerdt (2007), Walch    et al. (2015) , and  Kim & Ostriker (2017). It includes the cooling and heating processes relevant to the ISM, which produce a multiphase medium. They reproduce the vertical structure of the Galactic disk, which results from the dynamical equilibrium between the energy injected by the SNe and the gravitational potential of the disk. In particular, we used the set of simulations described in  Iffrig & Hennebelle (2017) , which have different levels of magnetization that we analyze to assess the role of the magnetic field in the orientation and characteristics of the Hi filaments.
Both simulations have an initial particle density of n 0 = 1. 5 cm -1. The initial magnetic field strengths are chosen around the median magnetic field strength 6. 0 ± 1. 8 µG observed in the CNM  (Heiles & Troland 2005). We selected snapshots at 75 and 81 Myr for the standard and very high magnetization cases, respectively, both of which are available in the simulation database Galactica (http://www. galactica-simulations. eu). This selection guarantees that the simulations have reached a quasi-steady state and does not affect the reported results. We present the synthetic observations in Fig. 16  and describe details of their construction in App. D.
Acknowledgements. JDS, HB, and YW acknowledge funding from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Consolidator Grant   CSF-648505. HB, JS, SCOG, and RK acknowledge support from the  Deutsche Forschungsgemeinschaft  via  SFB 881 , "The  Milky Way System " (subprojects  A1 ,  B1 ,  B2 , and  B8 ). SCOG and RK also acknowledge support from the  Deutsche Forschungsgemeinschaft via Germany's Excellence Strategy   EXC-2181/1-390900948  (the  Heidelberg STRUCTURES Cluster of Excellence ). RK also acknowledges funding from the  European Research Council  via the  ERC Synergy Grant  ECOGAL (grant  855130 ) and the access to the data storage service SDS@hd and computing services bwHPC supported by the  Ministry of Science, Research and the Arts Baden-Wrttemberg (MWK)  and the  German Research Foundation (DFG)  through grants  INST 35/1314-1  FUGG as well as INST 35/1134-1 FUGG.
NS acknowledges support by the  Agence Nationale de la Recherche (ANR)  and the  DFG  through the project " GENESIS"  ( ANR-16-CE92-0035-01/DFG1591/2-1 ). We thank the anonymous referee for the thorough review. We highly appreciate the comments, which significantly contributed to improving the quality of this paper. JDS thanks the following people who helped with their encouragement and conversation:  Peter G. Martin ,  Marc-Antoine Miville-Deschênes ,  Josh Peek ,  Jonathan Henshaw ,  Steve Longmore ,  Paul Goldsmith , and  Hans-Walter Rix. Part of the crucial discussions that lead to this work took part under the program  Milky-Way-Gaia  of the  PSI2  project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. This work has been written during a moment of strain for the world and its inhabitants.
Geophysical data inversion is a tool, which can be used to recover the subsurface distribution of physical properties from field data. Each type of geophysical data can be inverted using one or more inversion algorithms. In this paper, a set of geophysical magnetic and gravity data of the Hajjar area in Morocco, covering an extent of 3. 2 × 1. 6 km 2 , were used to make a 3D model of an orebody and to estimate the mineral reserve by potential-field geophysical data inversion and excess mass estimation. We thus promote the development and application of potential-field geophysical data inversion using the softwares Geosoft Oasis Montaj and Voxi Earth Modelling™ and the evaluation of its power compared to the excess mass estimation method. The process of inversion begins with data processing, then moves to analysis and interpretation, and ends with unconstrained Cartesian cut cell inversion.
The minimisation algorithm used in the majority of cases comes from the algorithm developed by  Beiner [1970]. The principle is to construct an error function. At each iteration of the inversion, the geologic model is calculated. The error function evaluates the difference between the measured data and the model response. As long as this difference remains large, the model parameters are varied one after the other until a set of parameters minimizing the error function is found. In this way, this approach is repeated for a number of iterations until one obtains the model whose response best satisfies the data. This principle can be compared to the analysis of a topographic surface  [Beiner, 1970, Fischer and Le Quang, 1981]  looking for the lowest point. Each iteration helps to descend along this topography and to converge towards the goal that represents the final model.
Gravity data The Hajjar gravity data that we were able to process included all the detailed calculations made by the Geological Survey of the Geology Directorate of Rabat. In addition, we created a database that contains the Bouguer anomaly for a regional density of 2. 67 g/cm 3. The 2. 67 g/cm 3 and 2. 3 g/cm 3 are the densities commonly used for gravity surveys in Morocco. The 2. 67 g/cm 3 density was used for the Hajjar area because it had not been used in previous studies, and the Bouguer correction depends on the dominant bedrock geology/composition for the area (a volcano-sedimentary formation), in addition, the maximum changes in elevation in the area covered by the gravity data is 117 m, which is small compared to the thickness of the sedimentary cover (124. 4 m) and the orebody depth (158 m)  [Bellott et al.
Gravity data over an infinite plane does not exist, and therefore, the best proxy is to utilize a dataset that extends markedly beyond the limits of any major sources of interest. Complete source isolation is unfortunately not possible in the natural world. For this reason, the separation of the gravitational anomalies of interest from other local and regional sources is often complex  [Jackson, 1996]. Gauss's law allows to calculate the total excess mass that produces a gravity anomaly, regardless of the distribution of this mass. Green found this solution to the excess mass within the Earth using a hemisphere for the hypothetical surface, S  [Siqueira et al. , 2017]. Assume that the top of the hemisphere is the surface of the Earth (z = 0). Then the surface integral can be divided into the top and the hemisphere  [Connor and Connor, 2009].
g. , gravity, magnetics) in 3D. It uses a Cartesian Cut Cell (CCC) inversion algorithm developed by  Ingram et al. [2003]. The algorithm has been simplified by  Ellis and MacLeod [2013]  in order to represent geological surfaces with greater accuracy. Inversions can be made as unconstrained  Roy et al. [2017]. Gravity and magnetic data (ground or airborne data, total field or gradient measurements) can be inverted using different methods  Barbosa and Pereira [2013] ,  Ellis and MacLeod [2013] ,  Farhi et al. [2016] ,  Ingram et al. [2003]. The data source can be provided in either Database or Grid format. For our case, we used the residual grids for above cases, gravity and magnetic data as sensor grid. Therefore, we were obliged to define the sensor elevation as grid files for the elevation sensors and choose the number of samples per cell.
Design of study and data collection We followed a semi-structured open-ended interview format for maximum information elicitation in our research design. This allowed us to develop a theory for the research setting with limited bias and high empirical testability. We aimed to collect data from firms based in locations with contrasting characteristics to be able to gather maximum insights about BDA capability development in diverse environments. France has a well-developed manufacturing industry and is a world leader in production and export of automobiles, aircrafts, defense products, etc. [33]. The country has also been rapidly expanding and developing its capability in the field of artificial intelligence (AI)  [34]. On the other hand, India is the world's fastest growing developing economy.
Hence, given the choice of our method and standardized practice of supply chain in most large global companies, 14 key informants are considered a sufficient sample size for the choice of data collection method employed in this paper. In the case of our research, we used 14 key informants who represent senior-most positions in the supply chain division of the firm. Hence, they represent not just personal experience of the issue but a firm-level perspective. As such, our data represents 14 firms. Additionally, the data gathering exercise was done in a semi-structured format to gather information about diverse perspectives on a topic so complex that a structured interview or survey would not have gathered the same. We also used third-party information sources like newspaper articles, Bloomberg supply chain reports, and French and Indian supply chain association newsletters to cross validate some claims (that could be publicly validated) and ensure the consistency and reliability of our dataset.
If those nuances were brought out during the free-flowing interview, they have been reported. In interest of qualitative interpretability, we have provided detailed information about the respondents. However, the interviews only dealt with the aspect of their firm's BDA capability and their firm's preparedness or response. The variety of interviewees and their backgrounds led to interesting and diverging viewpoints. While most executives felt the need for these technologies, many of them were unaware of the existence of these technologies in their current practices because they were packaged and bundled in ready-to-use software like SAP and ORACLE. The diverging opinion talked about the needless investment in this field for small and medium enterprises. Interviews were recorded and transcribed using Nvivo software to analyze and code the data.
Data coding The researchers moved between interviewing and data interpretation. Further needs of data were determined from the emerging theoretical insights in data analysis. Fig. 1  shows the complete process of coding from collected data. The process of interviewing and transcribing and coding was ongoing. More data was collected based on the theoretical saturation than the previously collected data provided. We stopped data collection when theoretical saturation was reached. The process took place between June 2017 and April 2018. The final transcription and coding were performed between February 2018 and April 2018. The focus on data, firm, and analytics, as well as its use by managers, was the theme of all interviews conducted during the process.
Skilled human resources and training for analytics Another factor that has been found to be instrumental for technology capability development at the corporate level is the presence of skilled resources  [44]. Bharadwaj  [44]  found that the lack of skilled resources can be detrimental to firms in their technology planning and capability enhancement. For evolving technologies, skilled resources are neither easily acquired nor developed. [45]. The lack of skilled resource availability can impede quick capability building in companies. During our data collection, we interviewed executives from companies based in different regions, including large and small cities like Paris, Bangalore, and Pune. These locations provided a contrast of opportunities for companies. Extant research in the field of skill and capability building has highlighted the importance of skilled resource availability in a large congregation of industries  [46].
Global connectedness Another aspect that pushes firms toward the use of such technologies is global connectedness. As Wu et al. [49]  found in their research, the necessity to reach more stakeholders is one of the earliest levers for digitization in organizations. Companies with a requirement to efficiently connect and disperse information to internal or external stakeholders will utilize more advanced technological capabilities for both analysis and dispersal of information. During our data collection exercise, we found that a company that was using Excel sheets for recordkeeping and analysis moved to Microsoft's PowerBI software to enhance their relationships with their stakeholders. While the company faced the need to adopt new technologies (i. e. , Tableau, Power BI, SAS), they were reluctant to make the required investment as the top management did not feel a strong need.
Contribution to practice In this paper, we have brought focus to the underlying factors that drive a company's BDA capability development. Through this research, we have extended the existing discourse on BDA by exploring beyond  the potential benefits of these technologies. Numerous research studies, as well as practitioner articles, have established the potential that BDA has in SCM. Studies also point out potential issues and challenges in implementation. Our study collects data from supply chain professionals to identify factors that lead to capability development in this growing technological field. Managers can also identify the factors that will be critical within their firm's context for further development of their BDA capability. As depicted in our study, many factors may seem underwhelming.
While due care was taken to ensure that the authors analyzed the data without any bias, unintended bias may occur. Further analysis, research and large-scale data based statistical studies would be required to confirm the results of our work. The third limitation of this work is the nature of the study. We performed a cross-sectional study in which we collected data from multiple firms and managers on the state of BDA capability development and issues. This methodology allowed us to cast a wide net. It was suitable for a first exploratory study. Such a research design was appropriate to collect all the factors that may affect a firm. Some of these factors may be more relevant to some firms as compared to others. To study capability development more closely, a longitudinal case study may be employed as a follow-up study.
Colonisation, speciation and extinction are dynamic processes that influence global patterns of species richness  [1] [2] [3] [4] [5]. The theory of island biogeography predicts that the contribution of these processes to the build-up of species diversity depends on habitat area and isolation  6, 7. Remarkably, to date there has been no globally robust test of this fundamental prediction 8 , because neither the appropriate data nor the analytical tools have been available. Here, we address both deficiencies to reveal, for island birds, the empirical shape of the general relationships that determine how colonisation, extinction and speciation rates vary with island area and isolation. We present the first global molecular phylogenetic dataset of times of colonisation and speciation of birds on islands, based on the terrestrial avifaunas of 41 oceanic archipelagos worldwide, including information on the 596 taxa on these archipelagos and their closest mainland relatives.
1 , Supplementary Data 1 and 2). We use archipelagos as our insular unit, because the high dispersal abilities of birds within archipelagos imply that for birds, archipelagos can be considered equivalent to single islands for less dispersive taxa  22  , and because archipelagos constitute the most appropriate spatiotemporal unit for framing analyses of biodiversity patterns at a large scale  2, 23. We extracted colonisation and speciation times for each archipelago from the phylogenetic trees, producing a 'global dataset' for the 41 archipelagos, which includes each archipelago's complete extant avifauna, plus all species known to have become extinct due to anthropogenic causes. The dataset comprises 596 insular taxa from 491 species. The phylogenies revealed a total of 502 archipelago colonisation events and 26 independent in-situ 'radiations' (cases where diversification has occurred within an archipelago) ranging in size from 2 to 33 species (the Hawaiian honeycreepers being the largest clade).
The distribution of colonisation times is summarised in Fig. 1  and the full dataset is given in Supplementary Data 1. Our new extension of the DAISIE framework allows us to estimate hyperparameters that control the relationship between archipelago area and isolation and archipelago-specific local CES rates, i. e. , rates of colonisation, cladogenesis (withinarchipelago speciation involving in situ lineage splitting), anagenesis (withinarchipelago speciation by divergence from the mainland without lineage splitting), natural extinction rates and carrying capacity. We tested the hypothesis that area and distance from the nearest mainland have an effect on the specific CES rates, and, where a significant effect was identified, estimated its shape and scaling. We developed a set of a priori models (Supplementary Information Table  1 ) where CES rates are power law functions of archipelago features.