Simple Data Model Research Articles

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

AbstractCamera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.

Learning Graph Databases: Neo4j an overview

As the internet is growing day by day, the amount of data being generated is huge. This data includes structured data and unstructured data. The data along with its relationship with other data makes the most powerful and meaningful information. Maximum data exists in the form of the relationship between different or same objects and the noticeable thing is the relationship between the data is more important than the data itself. These relationships are handled efficiently by Relational databases that store data having structures and which have several records. The important point to be noted here is that these Relational database management Systems use tables with normalization concept. If the amount of data in such tables is huge, then handling such a large amount of data with its relationships is a tedious task. Here, Graph Databases come into picture. Entities and their relationships in relational databases will be reflected with nodes and relationships in graph databases. Graph databases provide very simple data model than databases with Online Transaction Processing systems. Graph databases provide features such as transactional integrity and operational availability. This paper introduces the idea of graph database systems in conjunction with Neo4j encompassing its query features, consistency, transactions, availability, and scaling.

Unaware corporate social responsibility: impact of firm size, motivations and external pressures

ABSTRACT We explore differences in firms’ attitudes towards corporate social responsibility (CSR). Using a unique dataset covering 8,857 French firms, collected by the National Institute of Statistics and Economic Studies (INSEE), we identify firms conducting conscious CSR and others with effective but unaware CSR activities. We then construct three CSR pillar scores for each firm, using Mokken scale analysis, a form of non-parametric item response analysis. The CSR scores, along with responses to specific questions, allow us to characterize firms that implement conscious or unaware CSR. We then estimate simple probit and count data models to show that a significant share of firms are in fact actually engaged in unaware CSR, with no monotonic size effect. Cooperation with external actors such as NGOs mitigates the effect of firm size on the likelihood of conducting unaware CSR, while the effect of NGO campaigns against large firms is mainly to increase the environmental score of small firms in the same industry.

The interpolation phase transition in neural networks: Memorization and generalization under lazy training

Modern neural networks are often operated in a strongly overparametrized regime: they comprise so many parameters that they can interpolate the training set, even if actual labels are replaced by purely random ones. Despite this, they achieve good prediction error on unseen data: interpolating the training set does not lead to a large generalization error. Further, overparametrization appears to be beneficial in that it simplifies the optimization landscape. Here, we study these phenomena in the context of two-layers neural networks in the neural tangent (NT) regime. We consider a simple data model, with isotropic covariates vectors in d dimensions, and N hidden neurons. We assume that both the sample size n and the dimension d are large, and they are polynomially related. Our first main result is a characterization of the eigenstructure of the empirical NT kernel in the overparametrized regime Nd≫n. This characterization implies as a corollary that the minimum eigenvalue of the empirical NT kernel is bounded away from zero as soon as Nd≫n and, therefore, the network can exactly interpolate arbitrary labels in the same regime. Our second main result is a characterization of the generalization error of NT ridge regression including, as a special case, min-ℓ2 norm interpolation. We prove that, as soon as Nd≫n, the test error is well approximated by the one of kernel ridge regression with respect to the infinite-width kernel. The latter is in turn well approximated by the error of polynomial ridge regression, whereby the regularization parameter is increased by a “self-induced” term related to the high-degree components of the activation function. The polynomial degree depends on the sample size and the dimension (in particular on logn/logd).

Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve.

Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistance (CMVR). We sought to determine whether machine learning (ML) techniques could provide a patient-specific estimate of CMVR and therefore improve the accuracy of angio-FFR. Patients with chronic coronary syndromes underwent coronary angiography with FFR assessment. Vessel-specific CMVR was computed using a three-dimensional computational fluid dynamics simulation with invasively measured proximal and distal pressures applied as boundary conditions. Predictive models were created using non-linear autoregressive moving average with exogenous input (NARMAX) modelling with computed CMVR as the dependent variable. Angio-FFR (VIRTUheart™) was computed using previously described methods. Three simulations were run: using a generic CMVR value (Model A); using ML-predicted CMVR based upon simple clinical data (Model B); and using ML-predicted CMVR also incorporating echocardiographic data (Model C). The diagnostic (FFR ≤ or >0.80) and absolute accuracies of these models were compared. Eighty-four patients underwent coronary angiography with FFR assessment in 157 vessels. The mean measured FFR was 0.79 (±0.15). The diagnostic and absolute accuracies of each personalized model were: (A) 73% and ±0.10; (B) 81% and ±0.07; and (C) 89% and ±0.05, P < 0.001. The accuracy of angio-FFR was dependent in part upon CMVR estimation. Personalization of CMVR from standard clinical data resulted in a significant reduction in angio-FFR error.

Impulse Data Models for the Inverse Problem of Electrocardiography

To develop, train and test neural networks for predicting heart surface potentials (HSPs) from body surface potentials (BSPs). The method re-frames traditional inverse problems of electrocardiography into regression problems, constraining the solution space by decomposing signals with multidimensional Gaussian impulse basis functions. Impulse HSPs were generated with single Gaussian basis functions at discrete heart surface locations and projected to corresponding BSPs using a volume conductor torso model. Both BSP (inputs) and HSP (outputs) were mapped to regular 2D surface meshes and used to train a neural network. Predictive capabilities of the network were tested with unseen synthetic and experimental data. A dense full connected single hidden layer neural network was trained to map body surface impulses to heart surface Gaussian basis functions for reconstructing HSP. Synthetic pulses moving across the heart surface were predicted from the neural network with root mean squared error of 9.1±1.4%. Predicted signals were robust to noise up to 20 dB and errors due to displacement and rotation of the heart within the torso were bounded and predictable. A shift of the heart 40 mm toward the spine resulted in a 4% increase in signal feature localization error. The set of training impulse function data could be reduced, and prediction error remained bounded. Recorded HSPs from in-vitro pig hearts were reliably decomposed using space-time Gaussian basis functions. Activation times calculated from predicted HSPs for left-ventricular pacing had a mean absolute error of 10.4±11.4 ms. Other pacing scenarios were analyzed with similar success. Impulses from Gaussian basis functions are potentially an effective and robust way to train simple neural network data models for reconstructing HSPs from decomposed BSPs. The HSPs predicted by the neural network can be used to generate activation maps that non-invasively identify features of cardiac electrical dysfunction and can guide subsequent treatment options.

Розробка системи управління базою даних рекомендаційної системи для комп’ютерних та комп’ютерно-інтегрованих систем

The goal of this work is to develop a database management system of the recommendation system for computer networks and computer-integrated systems, as well as to compare the quality of its work with existing systems. Today, recommendation systems are widely used in computer networks, in particular, in social networks, Internet commerce systems, media content distribution, advertising, etc., as well as in computer-integrated systems, in particular, in the Internet of Things and smart houses. An effective way to present the data required for the recommendation system can reduce the number of resources required and facilitate the development and use of more sophisticated algorithms for compiling lists of recommendations. When storing data from the recommendation system, one of the important parameters of the database is the speed of reading/writing information, as well as the amount of memory required to store data in one format or another. Therefore, it is advisable to use simple data models. This paper investigated the feasibility and effectiveness of using open linear lists to store recommendation system data in computer networks and computer-integrated systems. To test the effectiveness of the proposed method of presenting data in the recommendation system, comparative experiments were conducted with such software as: relational database management system Postgresql, resident repository key-value pairs Redis and graph database Neo4j. Each method of presenting data was tested on the following indicators: time of filling the repository with test data; the amount of memory occupied by the repository after filling; recommendation generation time. The MovieLens data set was used as test data. The developed database management system based on linear lists is significantly ahead of the existing tools in terms of both speed and efficiency of memory use.

Utilising clinical data to personlaise boundary conditions significantly improves the accuracy of angiography based virtual FFR

Abstract Background Angiography-derived computed (virtual) fractional flow reserve (vFFR) permits the assessment of coronary physiology without the need for a pressure wire or hyperaemia. However, accuracy is limited by assumptions made about coronary microvascular resistance (CMVR) [1]. We hypothesised that machine learning may allow us to “tune” our estimate of the CMVR to increase the accuracy of vFFR. Purpose To determine whether routine clinical data can personalise CMVR and improve the accuracy of vFFR on an individual case basis. Methods Patients with chronic coronary syndromes underwent coronary angiography with FFR assessment. Vessel-specific CMVR was computed using a 3D- computational fluid dynamics simulation with invasively measured proximal and distal pressures. Predictive models were created using non-linear autoregressing moving average with exogenous inputs (NARMAX) modelling with computed CMVR as the dependent variable. vFFR was computed using previously described methods [2]. Three simulations were run, using: 1) a generic CMVR value (Model A); 2) a NARMAX-predicted CMVR based upon a panel of simple clinical data (Model B); and 3) a NARMAX-predicted CMVR incorporating echocardiographic data (Model C). The diagnostic and quantitative accuracy of each model was compared with directly measured FFR. Results Eighty four patients underwent coronary angiography with FFR assessment in 157 vessels. Mean age was 64 (±0.1) years and 64 (76%) were male. Mean FFR was 0.79 (±0.15). Mean CMVR was 1.01e+10 Pa/m3 s–1. vFFR error with Model A was ±0.10, with Model B was ±0.07 (p&amp;lt;0.001) and with Model C was ±0.05 (p&amp;lt;0.001) (Table 1). Conclusion vFFR is dependent upon not only the epicardial stenosis, but also the CMVR, estimation of which can be personalised based upon clinical and echocardiographic data. This can be used to increase the accuracy of vFFR. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): British Heart Foundation Clinial Research Training FellowshipNIHR i4i grant Table 1

Development of a Common Framework for Analysing Public Transport Smart Card Data

The data generated in public transport systems have proven to be of great importance in improving knowledge of public transport systems, being very valuable in promoting the sustainability of public transport through rational management. However, the analysis of this data involves numerous tasks, so that when the value of analysing the data is finally verified, the effort has already been very great. The management and analysis of the collected data face some difficulties. This is the case of the data collected by the current automated fare collection systems. These systems do not follow any open standards and are not usually designed with a multipurpose nature, so they do not facilitate the data analysis workflow (i.e., acquisition, storage, quality control, integration and quantitative analysis). Intending to reduce this workload, we propose a conceptual framework for analysing data from automated fare collection systems in mobility studies. The main components of this framework are (1) a simple data model, (2) scripts for creating and querying the database and (3) a system for reusing the most useful queries. This framework has been tested in a real public transport consortium in a Spanish region shaped by tourism. The outcomes of this research work could be reused and applied, with a lower initial effort, in other areas that have data recorded by an automated fare collection system but are not sure if it is worth investing in exploiting the data. After this experience, we consider that, even with the legal limitations applicable to the analysis of this type of data, the use of open standards by automated fare collection systems would facilitate the use of this type of data to its full potential. Meanwhile, the use of a common framework may be enough to start analysing the data.

The Relationship between Financial Inclusion and Monetary Policy: A Comparative Study of Countries’ in Sub-Saharan Africa and Latin America and the Caribbean

ABSTRACT Monetary policy ensures the financial system stabilization. Financial inclusion, characterized by the access and use of available financial services (credit, savings, payments, insurance at a low cost), by a broader population, may explain the effectiveness of the monetary policy, especially in developing countries. The objective of this study is to carry out a comparative analysis between the countries of Sub-Saharan Africa (SSA) and the countries of Latin America and the Caribbean (LAC), on the causal relationship between financial inclusion and monetary policy, as well as to assess the impact of financial inclusion over monetary policy. To support our study, we applied the panel vector autoregressive (PVAR) methodology, simple panel data models, and a feasible generalized least squares (FGLS) model. Results provide strong evidence of the existence of reverse causality between financial inclusion and monetary policy in both SSA and LAC, with monetary policy facilitating financial inclusion in both regions. Financial inclusion increases the effectiveness of monetary policy in SSA, while for LAC financial inclusion improves the efficiency of monetary policy. Thus, increased access to and use of financial services increase the efficiency of monetary policy in controlling inflation. Governments are encouraged to design or enhance policies that expand financial services, as well as to promote investment in financial inclusion in developing countries, to maintain the monetary system stability.

ICIPEMIR: Improving the Completeness, Interoperability and Patient Explanations of Medical Imaging Reports.

Although electronic health records have been facilitating the management of medical information, there is still room for improvement in daily production of medical report. Possible areas for improvement would be: to improve reports quality (by increasing exhaustivity), to improve patients' understanding (by mean of a graphical display), to save physicians' time (by helping reports writing), and to improve sharing and storage (by enhancing interoperability). We set up the ICIPEMIR project (Improving the completeness, interoperability and patients explanation of medical imaging reports) as an academic solution to optimize medical imaging reports production. Such a project requires two layers: one engineering layer to build the automation process, and a second medical layer to determine domain-specific data models for each type of report. We describe here the medical layer of this project. We designed a reproducible methodology to identify -for a given medical imaging exam- mandatory fields, and describe a corresponding simple data model using validated formats. The mandatory fields had to meet legal requirements, domain-specific guidelines, and results of a bibliographic review on clinical studies. An UML representation, a JSON Schema, and a YAML instance dataset were defined. Based on this data model a form was created using Goupile, an open source eCRF script-based editor. In addition, a graphical display was designed and mapped with the data model, as well as a text template to automatically produce a free-text report. Finally, the YAML instance was encoded in a QR-Code to allow offline paper-based transmission of structured data. We tested this methodology in a specific domain: computed tomography for urolithiasis. We successfully extracted 73 fields, and transformed them into a simple data model, with mapping to a simple graphical display, and textual report template. The offline QR-code transmission of a 2,615 characters YAML file was successful with simple smartphone QR-Code scanner. Although automated production of medical report requires domain-specific data model and mapping, these can be defined using a reproducible methodology. Hopefully this proof of concept will lead to a computer solution to optimize medical imaging reports, driven by academic research.

Resolving Pitching Airfoil Transonic Aerodynamics by Computational Fluid Dynamics Data Modeling1

Abstract A detailed numerical study of harmonically pitching airfoils of NACA00 series is presented here. Based on the analysis of the computational fluid dynamics (CFD) results, a hypothesis is made that a simple data model can capture the dynamics of the airfoils in pitch. The data model is based on the cl−α (lift coefficient–angle of attack) hysteresis loops that retain generic geometrical characteristics for a wide range of reduced frequency, k, encountered in flutter in transonic flows for all the NACA00 airfoils considered. The model was trained on a subset of the considered NACA00 airfoils and then tested on the remaining NACA00 set, for a subset of the reduced frequencies. The model predictions of the cl−α hysteresis loops for the test set are shown to be in excellent agreement with the CFD results for the range of k typical of transonic flutter. The data model offers a paradigm shift in the prediction of transonic flow dynamics of pitching airfoils and will guide the development of a new transfer function that will be incorporated in a new aeroelastic framework leading to an appropriate transonic flutter model for use in the development of future aircraft.

Identification in simple binary outcome panel data models

SummaryThis paper first reviews some of the approaches that have been taken to estimate the common parameters of binary outcome models with fixed effects. We limit attention to situations in which the researcher has access to a data set with a large number of units (individuals or companies, for example) observed over a number of time periods. We then apply some of the existing approaches to study fixed-effects panel data versions of entry games, like the ones studied in Bresnahan and Reiss (1991) and Tamer (2003).

Characterizing variability and predictability for air pollutants with stochastic models.

We investigate the dynamics of particulate matter, nitrogen oxides, and ozone concentrations in Hong Kong. Using fluctuation functions as a measure for their variability, we develop several simple data models and test their predictive power. We discuss two relevant dynamical properties, namely, the scaling of fluctuations, which is associated with long memory, and the deviations from the Gaussian distribution. While the scaling of fluctuations can be shown to be an artifact of a relatively regular seasonal cycle, the process does not follow a normal distribution even when corrected for correlations and non-stationarity due to random (Poissonian) spikes. We compare predictability and other fitted model parameters between stations and pollutants.

Multi-domain Collection Management Simplified — the Finnish National Collection Management System Kotka

Many natural history museums share a common problem: a multitude of legacy collection management systems (CMS) and the difficulty of finding a new system to replace them. Kotka is a CMS developed starting in 2011 at the Finnish Museum of Natural History (Luomus) and Finnish Biodiversity Information Facility (FinBIF) (Heikkinen et al. 2019, Schulman et al. 2019) to solve this problem. It has grown into a national system used by all natural history museums in Finland, and currently contains over two million specimens from several domains (zoological, botanical, paleontological, microbial, tissue sample and botanic garden collections). Kotka is a web application where data can be entered, edited, searched and exported through a browser-based user interface. It supports designing and printing specimen labels, handling collection metadata and specimen transactions, and helps support Nagoya protocol compliance. Creating a shared system for multiple institutions and collection types is difficult due to differences in their current processes, data formats, future needs and opinions. The more independent actors there are involved, the more complicated the development becomes. Successful development requires some trade-offs. Kotka has chosen features and development principles that emphasize fast development into a multitude of different purposes. Kotka was developed using agile methods with a single person (a product owner) making development decisions, based on e.g., strategic objectives, customer value and user feedback. Technical design emphasizes efficient development and usage over completeness and formal structure of the data. It applies simple and pragmatic approaches and improves collection management by providing practical tools for the users. In these regards, Kotka differs in many ways from a traditional CMS. Kotka stores data in a mostly denormalized free text format and uses a simple hierarchical data model. This allows greater flexibility and makes it easy to add new data fields and structures based on user feedback. Data harmonization and quality assurance is a continuous process, instead of doing it before entering data into the system. For example, specimen data with a taxon name can be entered into Kotka before the taxon name has been entered into the accompanying FinBIF taxonomy database. Example: simplified data about two specimens in Kotka, which have not been fully harmonized yet. Taxon: Corvus corone cornix Country: FI Collector: Doe, John Coordinates: 668, 338 Coordinate system: Finnish uniform coordinate system Taxon: Corvus corone cornix Country: FI Collector: Doe, John Coordinates: 668, 338 Coordinate system: Finnish uniform coordinate system Taxon: Corvus cornix Country: Finland Collector: Doe, J. Coordinates: 60.2442, 25,7201 Coordinate system: WGS84 Taxon: Corvus cornix Country: Finland Collector: Doe, J. Coordinates: 60.2442, 25,7201 Coordinate system: WGS84 Kotka’s data model does not follow standards, but has grown organically to reflect practical needs from the users. This is true particularly of data collected in research projects, which are often unique and complicated (e.g. complex relationships between species), requiring new data fields and/or storing data as free text. The majority of the data can be converted into simplified standard formats (e.g. Darwin Core) for sharing. The main challenge with this has been vague definitions of many data sharing formats (e.g. Darwin Core, CETAF Specimen Preview Profile (CETAF 2020), allowing different interpretations. Kotka trusts its users: it places very few limitations on what users can do, and has very simple user role management. Kotka stores the full history of all data, which allows fixing any possible errors and prevents data loss. Kotka is open source software, but is tightly coupled with the infrastructure of the Finnish Biodiversity Information Facility (FinBIF). Currently, it is only offered as an online service (Software as a Service) hosted by FinBIF. However, it could be developed into a more modular system that could, for example, utilize multiple different database backends and taxonomy data sources.

A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks

Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and Instagram. Compared to other OSNs, it has a simple data model and a straightforward data access API. This makes it ideal for social network studies attempting to analyze the patterns of online behavior, the structure of the social graph, the sentiment towards various entities and the nature of malicious attacks in a vivid network with hundreds of millions of users. Indeed, Twitter has been established as a major research platform, utilized in more than ten thousands research articles over the last ten years. Although there are excellent review and comparison studies for most of the research that utilizes Twitter, there are limited efforts to map this research terrain as a whole. Here we present an effort to map the current research topics in Twitter focusing on three major areas: the structure and properties of the social graph, sentiment analysis and threats such as spam, bots, fake news and hate speech. We also present Twitter’s basic data model and best practices for sampling and data access. This survey also lays the ground of computational techniques used in these areas such as Graph Sampling, Natural Language Processing and Machine Learning. Along with existing reviews and comparison studies, we also discuss the key findings and the state of the art in these methods. Overall, we hope that this survey will help researchers create a clear conceptual model of Twitter and act as a guide to expand further the topics presented.

Helios

Helios is a distributed, highly-scalable system used at Microsoft for flexible ingestion, indexing, and aggregation of large streams of real-time data that is designed to plug into relational engines. The system collects close to a quadrillion events indexing approximately 16 trillion search keys per day from hundreds of thousands of machines across tens of data centers around the world. Helios use cases within Microsoft include debugging/diagnostics in both public and government clouds, workload characterization, cluster health monitoring, deriving business insights and performing impact analysis of incidents in other large-scale systems such as Azure Data Lake and Cosmos. Helios also serves as a reference blueprint for other large-scale systems within Microsoft. We present the simple data model behind Helios, which offers great flexibility and control over costs, and enables the system to asynchronously index massive streams of data. We also present our experiences in building and operating Helios over the last five years at Microsoft.

Towards scalable dataframe systems

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in R and Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant ambiguity regarding dataframe semantics. In this paper we lay out a vision and roadmap for scalable dataframe systems. To demonstrate the potential in this area, we report on our experience building Modin, a scaled-up implementation of the most widely-used and complex dataframe API today, Python's pandas. With pandas as a reference, we propose a simple data model and algebra for dataframes to ground discussion in the field. Given this foundation, we lay out an agenda of open research opportunities where the distinct features of dataframes will require extending the state of the art in many dimensions of data management. We discuss the implications of signature dataframe features including flexible schemas, ordering, row/column equivalence, and data/metadata fluidity, as well as the piecemeal, trial-and-error-based approach to interacting with dataframes.

Two Models of Double Descent for Weak Features

The "double descent" risk curve was proposed to qualitatively describe the out-of-sample prediction accuracy of variably-parameterized machine learning models. This article provides a precise mathematical analysis for the shape of this curve in two simple data models with the least squares/least norm predictor. Specifically, it is shown that the risk peaks when the number of features $p$ is close to the sample size $n$, but also that the risk decreases towards its minimum as $p$ increases beyond $n$. This behavior is contrasted with that of "prescient" models that select features in an a priori optimal order.

Expressing History through a Geo-Spatial Ontology

Conventional Geographical Information Systems (GIS) software struggles to represent uncertain and contested historical knowledge. An ontology, meaning a semantic structure defining named entities, and explicit and typed relationships, can be constructed in the absence of locational data, and spatial objects can be attached to this structure if and when they become available. We describe the overall architecture of the Great Britain Historical GIS, and the PastPlace Administrative Unit Ontology that forms its core. Then, we show how particular historical geographies can be represented within this architecture through two case studies, both emphasizing entity definition and especially the application of a multi-level typology, in which each “unit” has an unchanging “type” but also a time-variant “status”. The first includes the linked systems of Poor Law unions and registration districts in 19th century England and Wales, in which most but not all unions and districts were coterminous. The second case study includes the international system of nation-states, in which most units do not appear from nothing, but rather gain or lose independence. We show that a relatively simple data model is able to represent much historical complexity.