Search

Although the Ordnance Survey has itself been the subject of historical research, scholars have not systematically used its maps as primary sources of information. This is partly for disciplinary reasons and partly for the technical reason that high-quality maps have not until recently been available digitally, geo-referenced, and in color. A final, and crucial, addition has been the creation of item-level metadata which allows map collections to become corpora which can for the first time be interrogated en masse as source material. By applying new Computer Vision methods leveraging machine learning, we outline a research pipeline for working with thousands (rather than a handful) of maps at once, which enables new forms of historical inquiry based on spatial analysis. Our 'patchwork method' draws on the longstanding desire to adopt an overall or 'complete' view of a territory, and in so doing highlights certain parallels between the situation faced by today's users of digitized maps, and a similar inflexion point faced by their predecessors in the nineteenth century, as the project to map the nation approached a form of completion. [ABSTRACT FROM AUTHOR]

We present obspyDMT, a free, open-source software toolbox for the query, retrieval, processing and management of seismological data sets, including very large, heterogeneous and/or dynamically growing ones. ObspyDMT simplifies and speeds up user interaction with data centers, in more versatile ways than existing tools. The user is shielded from the complexities of interacting with different data centers and data exchange protocols and is provided with powerful diagnostic and plotting tools to check the retrieved data and metadata. While primarily a productivity tool for research seismologists and observatories, easy-to-use syntax and plotting functionality also make obspyDMT an effective teaching aid. Written in the Python programming language, it can be used as a stand-alone command-line tool (requiring no knowledge of Python) or can be integrated as a module with other Python codes. It facilitates data archiving, preprocessing, instrument correction and quality control - routine but nontrivial tasks that can consume much user time. We describe obspyDMT's functionality, design and technical implementation, accompanied by an overview of its use cases. As an example of a typical problem encountered in seismogram preprocessing, we show how to check for inconsistencies in response files of two example stations. We also demonstrate the fully automated request, remote computation and retrieval of synthetic seismograms from the Synthetics Engine (Syngine) web service of the Data Management Center (DMC) at the Incorporated Research Institutions for Seismology (IRIS). [ABSTRACT FROM AUTHOR]