dataesr/scientific-paragraphs-categorization huggingface dataset:
7 program  [40, 41]. The accessible decays modes of the final-state superparticles and associated branching ratios were calculated internally by the HERWIG++ program. No 4body modes were considered and therefore in the highly compressed region with m t1 < m b + m W + m χ0 1 the dominant decay channel of the stop was 2 t1 → c χ0 1. In regions of phase space where m t1 > m b + m W + m χ0 1 , the decay mode t1 → W + b χ0 1 was found to dominate. For all mass scenarios considered in this study, the dominant gluino decay channel was g → q q χ0 1. Finally, when simulating the production of a top squark, top quark and neutralino, a global normalization factor of K = 1. 4 has been applied to the total cross section. This factor aims to account for the large next-to-leading order contributions to this process  [42].
0  [51, 52]  package has been used. We have followed the MENLOPS prescription to match an event sample based on NLO matrix elements related to the production of a single gauge boson to leading-order (LO) samples describing the production of the same gauge boson with one and two extra jets  [53, 54]. In all cases, the vector bosons have been forced to decay either leptonically or invisibly, including all three flavours of leptons, and matrix elements have been convoluted with the CTEQ6M PDF set. Moreover, the invariant masses of lepton pairs produced via a γ * /Z-boson s-channel diagram have been required to exceed 10 GeV. The production of a W -boson with heavy flavour jets has been simulated separately using MADGRAPH 5 and its builtin Standard Model implementation. We have generated LO matrix elements that have been convoluted with the LO set of parton densities CTEQ6L1  [39].
47 (2. 5) and they must be isolated such that the sum of the transverse momenta of all charged particles in a cone of radius ∆ R < 0. 2 5 centered on the lepton is less than 10% of its transverse momentum. Jets are reconstructed from all visible final-state particles with a pseudorapidity satisfying |η j | < 4. 9 by applying an anti-k T jet algorithm  [57]  with a radius parameter R = 0. 4, as implemented in the FASTJET program  [58]. We select reconstructed jet candidates that do not overlap with candidate electrons within a distance of ∆ R < 0. 2, and with a transverse momentum p j T > 20 GeV and a pseudorapidity |η j | < 2. 5. Any lepton candidate within a distance ∆ R < 0. 4 to the closest of the selected jets is then discarded. We further identify jets as originating from a b-quark if their angular distance to a B-hadron satisfies ∆ R < 0.
Acknowledgements We are grateful to the organizers of the 2013 Les Houches Physics at TeV Colliders workshop where this work has been initiated, as well as to  Filip Moortgat  for enlightening discussions. he work of BF has been partially supported by the  Theory-LHC France initiative  f the  CNRS/IN2P3. AW and PR acknowledge the support of the  European Union via MCNet ,  PITN-GA-2012-315877  and the  Science and Technology Facilities Council.
The vast amount of knowledge about past human societies has not been systematically organized and, therefore, remains inaccessible for empirically testing theories about cultural evolution and historical dynamics. For example, what evolutionary mechanisms were involved in the transition from the small-scale, uncentralized societies, in which humans lived 10,000 years ago, to the large-scale societies with an extensive division of labor, great differentials in wealth and power, and elaborate governance structures of today? Why do modern states sometimes fail to meet the basic needs of their populations? Why do economies decline, or fail to grow? In this article, we describe the structure and uses of a massive databank of historical and archaeological information, Seshat: The Global History Databank.
Today, almost a century since Breasted wrote these words, his grand vision remains unfulfilled. The vast amount of knowledge about past human societies, held collectively by thousands of historians and archaeologists, has not been systematically organized. This knowledge is scattered across heterogeneous databases, innumerable books, publications in academic journals, and reports in the 'grey' literature, as well as notes in the private archives of scholars. Much of it is not even written, but resides in the heads of various experts and is permanently lost when they pass away. The store of knowledge about past societies is now much more immense than it was in 1919, but it remains inaccessible for answering Big Questions about us and our societies, such as the one formulated by  Breasted. Translating his question into more modern terms, we might state it as follows.
This body of organized knowledge will enable us to see much more clearly, if not fully recover, the lost chapters of a human career, thus fulfilling Breasted's dream. This paper describes our vision for accomplishing this goal. We will do so by building a massive databank of historical and archaeological information, which we call Seshat: the Global History Databank. As this paper was written (in the first half of 2015), Seshat was rapidly becoming much more than a vision. Thanks to the generous support from government funding agencies, private foundations, and individual supporters (see Acknowledgments), we have already started the job of building it. The goal of this article, thus, is to describe the structure and uses of Seshat. We start with a theoretical background-a brief overview of various theories that have been advanced to explain the emergence of large-scale, complex societies over the last 10,000 years or so, and the changing levels of inequality in the long-term history of our species.
Because different theories postulate different causal factors responsible for the evolution of social complexity, an analysis of which potentially explanatory variables correlate best with rising social complexity has a direct bearing on the empirical adequacy of rival theories. Moreover, we can do better than employing a purely correlational approach. Because causes precede effects, different theories make divergent predictions about temporal sequences of events. Thus, we need a dynamical databank that allows us to trace how different characteristics of past societies changed with time and determine which variables' changes precede changes in other variables. Of course, such an approach will not work in situations where temporal changes cannot be resolved on a sufficiently fine temporal scale; however, the continuous improvement in our methodologies of studying the past gives us hope that there will be a growing ensemble of case studies in which temporal resolution is sufficient for the purpose of testing theories.
Building the Databank Our long-term goal is for Seshat to be a vast repository for structured data on theoretically relevant variables for any past human society, for which such data exist. Our time frame is the last 10,000 years of human history (i. e. , from the Neolithic to the present). 'Theoretically relevant' variables are those invoked by theories about how human societies evolve and function. Such variables either describe the phenomenon that we want to explain, or something that helps to explain the phenomenon: explanandum (for example, the trajectories of social complexity, or of inequality) and explanans, respectively  (Hempel and Oppenheim 1948). Some variables may play both roles, being interesting in their own right and also having an explanatory potential for other aspects of social dynamics.
Because Seshat will eventually become an active databank, incorporating tools for automating the harvesting of variable values from Internet-accessible sources (e. g. historical and archaeological databases, DBPedia, JSTOR), the set of such variables will expand as new theories are proposed, invoking new explanatory factors. Seshat is a theory-neutral databank; future investigators will also have the freedom to re-conceptualize and redefine variables using their preferred theoretical frameworks.
Information Architecture of the Seshat Databank The basic entity classes (types) in the databank's information architecture are Organizations and Territories, each of which has a set of temporally-scoped Variables associated with it. Entity classes serve as units of data collection and entry and as potential units of statistical analysis. Territories have fixed geographical bounds that do not change with time, whereas Organizations are defined by temporal bounds and may be associated with specific Territories at specific intervals. A subset of variables relate Organizations to Territories, and the temporal dynamics of these Relationship Variables allow us to capture both temporal and geographical dynamics of the features of human societies. One of the most important relationship variables is the controls relation that specifies which social organization controls which territories over time.
A sub-polity is basically a polity that lacks sovereignty. As the databank evolves, and more questions are posed, it is conceivable that other kinds of Territory and Organization could be added. Additionally, we recognize that most historical polities did not have sharp boundaries. Instead, in many cases, their spheres of control gradually declined from the political center, or centers. There also could be effectively stateless lacunae inside the territory nominally claimed by a state. The initial implementation of the databank does not reflect such 'facts on the ground,' but the information architecture supports it through the sub-polity class-an area that is related to a specific territory, which differs in significant ways from the rest of the polity. As a coding convention, we prefer to limit the temporal extent of polities to approximately two or three centuries, because polities tend to evolve with time and often experience transformative events; thus, it is natural to code the next period as a different polity.
For example, we divide Roman history into the following polities: Regal; Early, Middle, and Late Republic; Principate, and Dominate. The current version of the Seshat Code Book has been developed primarily for historical societies. Coding data for societies that are known only archaeologically poses an additional set of challenges. We are currently developing an archeological Seshat Code Book that will address these challenges  (Marciniak et al. , forthcoming; Palmisano et al. forthcoming).
Coding Procedure In populating the databank, the following instructions are provided to coders (using approaches based on NGAs as an example): • Identify an NGA within the larger World Region. This should be an area ideally around 100 by 100 km, or 10,000 sq. km. Dimensions, of the NGA, however, are allowed to vary quite substantially. • In the NGA data page, list chronologically all polities that were located in the NGA, or encompassed it (see Latium as an example). For periods when the NGA was fragmented among many small-scale polities, use the quasi-polity approach. In the intermediate case, when there were several large polities (for example, the NGA was on a frontier between two states), focus on the one that controlled the largest proportion of the NGA. • As a coding convention, we try not to have too-long chunks of time on the same polity data sheet.
The current version of the Seshat Codebook is included in the Supplementary Materials. The historical and archaeological records for even the best-attested societies are incomplete, so we need to deal with the issue of missing data. Therefore, for each variable of interest, we collect data on a number of different measures, and a certain degree of redundancy in these variables is implemented by design. For many past societies (some of which are known only archaeologically), we will not be able to code every variable. Thus, different variables can serve as proxies for the same underlying factor, enabling us to compare different societies even in the face of missing data. For example, estimating populations of historical states and empires is a notoriously difficult problem; as a result, we developed a number of proxies that correlate with population numbers (e.
Seshat Wiki: an Initial Implementation of the Databank During 2012, our group developed a flexible architecture for this databank. The databank has two distinct aspects: textual/descriptive sections (including references), and coded data. The text-based sections describe what is known about a particular variable based on previous scholarship. These 'thick' descriptions provide important context about the variables being addressed, the sources of information used, and make explicit how a decision was made on a particular coding (for a more extended discussion of these issues, see Hoyer and Manning, forthcoming). The coded data are based on the narrative sections, and enable this information to be translated into a form that is amenable to statistical analysis. The databank therefore combines the best features of traditional humanistic and scientific approaches to investigating the past.
The databank is implemented as a 'Wiki' (i. e. , a web-based application that enables an open access, collaborative project), which allows text on different variables to be easily added and updated. The information from the Wiki is 'scraped' automatically and translated into formats (e. g. , tab-delimited files) that can be analyzed in statistical software packages. Although the Wiki implementation was a suitable approach during the initial phases of the databank's development, it is rapidly becoming a limiting factor due to the growth of data that is added daily. As of the writing of this paper, we have developed a plan to port the Wiki data into an RDF-based triplestore  (Schreiber and Raimond 2014). This type of graph-based representation is particularly suitable for capturing rich, structured knowledge about complex entities and multi-faceted relationships between them.
Seshat Databank: Overarching Plan The design goal is to maximize the research community's collective intelligence while minimizing overheads. Ultimately, we aim to create a system with easy-touse software tools that support the following roles and features: Seshat Contributors. Non-technical users, such as historians and archaeologists, can easily add data to the system and update existing data (e. g. , using graphical input tools). Ultimately, we would like to make this open and robust enough so that any web user can suggest updates to the datasets in an easy way (crowd-sourcing). Seshat Editors. Data administrators can moderate, correct, and manage the data in the system over time. The downside to harvesting data from a broad community is that inaccuracies, disagreements, user errors, and malicious use must be managed or else the dataset will degrade in quality over time.
Seshat Data Architects. Knowledge engineers can make changes to the schema (data structures) over time and manage transitions between versions of the schema without breaking databank integrity (maintaining and assuring the accuracy and consistency of data over its entire life-cycle). Seshat Analysts. Statisticians and mathematical modelers will prepare and analyze time series data to investigate big questions about human societies. Seshat Readers. General end-users can browse, search, download, and view arbitrary slices of the data in a very wide range of attractive and helpful ways. Seshat Administrators. Technical administrators can manage the data curation and publication platform or servers to deal with changes in data, schemata, collection tools and publication formats or tools (e. g.
, visualizations) over time in a scalable fashion. Seshat will be managed through the Dacura Linked Data platform (http://dacura. scss. tcd. ie), developed at Trinity College Dublin. Dacura provides support for dataset capturing, curation, and publication (Feeney 2014). Nonetheless, when dealing with a complex and multi-faceted domain, the development of formal schema and tools to facilitate convenient and accurate data input requires considerable experimental and developmental effort. The nature of the Seshat data is such that there are many opportunities to take advantage of maps and timelines to capture the spatial and temporal aspects of the data. However, these tasks are labor-intensive, so the development of the databank system will proceed incrementally. The Seshat databank will be progressively migrated from the current Wiki to the Dacura Linked Data platform in a number of phases, each designed to add functionality to the system, progressively increasing its ability to gather and present high quality structured data, without interrupting researchers' ability to add new data.
Phase 1: Data Validation and Extraction, Seshat Data Web The first phase of development will retain the current Wiki as the authoritative source of data and the data-input tool. It will develop a RDF schema (or knowledge model), which will be used to validate the data in the Wiki and prepare the ground for future migration. This involves two parallel development tasks. First, we must develop the first version of an RDF-based Seshat Knowledge Model, including definition of vocabularies and formal specification of relationships between concepts. This will be an incremental process, starting with the core concepts and working outwards to the details. Second, we must develop two tools which (a) extract the structured, natural language data from the wiki into a time series data dump amenable to statistical data analysis and (b) formally validate the data in the wiki to eliminate coding errors and remove ambiguity.
These two tools enable us to perform analysis on the data; to track the size and growth of the databank; to detect inconsistencies in the data set; to copy the data from the wiki to a complementary graph-based database (RDF triplestore); and to generate data problem reports for the Seshat Contributors and Editors. The data problem reports will enable Editors or Contributors to correct and improve the data in the wiki and the Seshat Code Book. In addition to these tasks, we will use the existing facilities of the Dacura platform to perform more extensive (semantic) data quality checks, using the RDF schema to ensure that any updated data conforms to the knowledge model, and publish the RDF dataset as a simple Linked Data site. This system will operate in parallel to the existing Wiki so current work practices and data ownership can continue.
In addition, our Data Export tool allows Seshat Editors to view the data as it is being compiled and Seshat Analysts to begin to work on modeling problems. Further Phases. The goal is to progressively replace the Wiki input format with graphical user interface tools which use the schema definition to maintain data quality over time and to ease the task of creating and updating complex instances of data types. We will progressively add enhancements to the publishing capabilities-developing richer interfaces for browsing, visualizing, analyzing, and interlinking the Seshat databank with external sources of knowledge. Once this process is sufficiently advanced, we will switch the authoritative source of data from the wiki to the triplestore, retaining the Wiki as a publishing outlet.
The Future of Seshat As described in the previous section, at the time of writing this paper, the main focus of the Seshat Databank development team is on coding the NGAs in the World Sample-30 for a set of variables defined by the current list of Big Questions. The variable classes include social complexity, warfare, rituals, agriculture and resources, institutions and equity, and economics and well-being (see the current version of the Code Book in the Supplementary Online Materials for a detailed description of the variables). Simultaneously, we are making a transition from the current Wiki to the Seshat triplestore managed through the Dacura Linked Data platform. Accomplishing these near-term goals will provide us with a solid basis from which to progressively expand the Databank in multiple mutually supportive directions.
The two most important dimensions of Seshat expansion in the medium term are (1) adding more variables and (2) increasing the databank coverage to a progressively greater fraction of the world's surface. As far as new variables are concerned, it would be extremely interesting to add data on the evolution of technology and on linguistic evolution. These variable classes are interesting in themselves because each is associated with a developed body of theory (for recent reviews, see  Boyd et al. 2013 , Gray et al. 2013) , and they can be important explanans variables in helping us explain other aspects of the cultural evolution of human societies. Another exciting area of research is gene-culture coevolution, which has, so far, been primarily investigated with theoretical models. There is little reason to doubt that the ongoing methodological advances in ancient DNA will soon enable us to test these theories with data  (Callaway 2015).
Increasing the thematic and geographic coverage, however, will come at a cost. Projecting from the current rate of data accumulation, we estimate that by 2017, the size of the Seshat databank will approach-or exceed-the symbolic threshold of one million 'facts. ' 3 But even this enormous amount of data will be restricted in its thematic and, especially, geographic coverage. It is already clear that extending the geographic coverage to the whole world is not feasible using our current data collection approach. One direction that we are currently exploring is crowd sourcing-developing software that will support recruitment of volunteers to assist in manual data processing. More radically, we will need to transition to a technology that automates the harvesting of the required variable values from open web-sources, including, but not limited to, the repositories of scholarly publications.
Thus, we intend to explore the possibility of harvesting data from such source as JSTOR's archive of academic publications. While harvesting data from unstructured massive repositories, such as JSTOR, is a future direction of Seshat development, there are also numerous sources of structured data available, which contain data relevant to the Seshat Databank. The Linked Data technology, on which the Dacura platform is based, facilitates interlinking the Seshat data with other datasets and supports applications and analysis that integrates Seshat data with other sources, as long as they also use the RDF technology. Given the richness of the Seshat databank and the expanding web of data, there will be many important insights to be gained by interlinking data that go beyond the scope of the Seshat big questions themselves.
Another very important direction in which to develop is improved data quality. Once the databank has been migrated to the triplestore, each data point in it will have searchable, structured provenance records associated with it, which will detail the precise source of each variable value. This will go beyond logging information on data changes and will connect data values with the source of academic research or primary materials. For example, we can have multiple alternative estimates of the population of some polity at a certain point in time: one from a contemporary source, such as a tax survey; others based on opinions of different experts, which may strongly disagree with one another; and yet another based on the estimate of the maximum carrying capacity given the contemporary agricultural technology.