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To rescue at-risk historical scientific data stored at the McGill Observatory, the objectives of the Data Rescue Archive Weather (DRAW) project are: to build a repository; to develop a protocol to preserve the data in weather registers; and to make the data available to research communities and the public. The paper aims to discuss these issues.

The DRAW project adopts an open archive information system compliant model as a conceptual framework for building a digital repository. The model consists of data collection, conversion, data capture, transcription, arrangement, description, data extraction, database design and repository setup.

A climate data repository, as the final product, is set up for digital images of registers and a database is designed for data storage. The repository provides dissemination of and access to the data for researchers, information professionals and the public.

Doing a quality check is the most important aspect of rescuing historical scientific data to ensure the accuracy, reliability and consistency of data.

The DRAW project shows how the use of historical scientific data has become a key element in research analysis on scientific fields, such as climatology and environmental protection.

The historical climate data set of the McGill Observatory is by nature unique and complex for preservation and research purposes. The management of historical scientific data is a challenge to rescue and describe as a result of its heterogeneous and non-standardized form.

This paper discusses data-collection strategies that use digitized historical newspaper archives to study social conflicts and social movements from a global and historical perspective focusing on nationalist movements. I present an analysis of State-Seeking Nationalist Movements (SSNMs) dataset I, which includes news articles reporting on state-seeking activities throughout the world from 1804 to 2013 using the New York Times and the Guardian/Observer. In discussing this new source of data and its relative value, I explain the various benefits and challenges involved with using digitized historical newspaper archives for world-historical analysis of social movements. I also introduce strategies that can be used to detect and minimize some potential sources of bias. I demonstrate the utility of the strategies introduced in this paper by assessing the reliability of the SSNM dataset I and by comparing it to alternative datasets. The analysis presented in the paper also compares the labor-intensive manual data-coding strategies to automated approaches. In doing so, it explains why labor-intensive manual coding strategies will continue to be an invaluable tool for world-historical sociologists in a world of big data.

How do we make sense of urban life in the past? What do we do when we study urban history, and to what extent do our methods fully capture the complexities of historical city living? These are crucial questions for any scholar interested in the historical dimensions of urban experience. Notwithstanding the interest of most urban historians in the relationship between the physical form of urban space and its experience by inhabitants and visitors, very few scholars have written histories that systematically integrate these two areas of inquiry. In this chapter, we argue that such research requires a method and an accompanying tool that can analyze historical urban life in a more integrated, holistic way. We propose a way forward by introducing the Time Machine platform as a scalable data visualization and analysis tool for researching everyday urban experience across space and time. To illustrate the potential we focus on a case study: the area of the Bloemstraat in early modern Amsterdam. Unpacking a section of the Bloemstraat, house by house and room by room, we show how the Time Machine forms an instrument to connect spatial layouts to the arrangement of objects and to the practical and social use of the space by the inhabitants and visitors. We also sketch how this tool illuminates more dynamic spatial and temporal practices such as how people, goods, and activities are connected to locations in the wider city and beyond.

The purpose of this paper is to create a longitudinal data-driven model of change over time in a postindustrial landscape, using the “Copper Country” of Michigan’s Upper Peninsula as a case study. The models resulting from this project will support the heritage management and public education goals of the contemporary communities and Keweenaw National Historical Park that administer this nationally significant mining region through accessible, engaging, and interpretable digital heritage.

The paper applies Esri’s CityEngine procedural modeling software to an existing historical big data set. The Copper Country Historical Spatial Data Infrastructure, previously created by the HESA lab, contains over 120,000 spatiotemporally specific building footprints and other built environment variables. This project constructed a pair of 3D digital landscapes comparing the built environments of 1917 and 1949, reflecting the formal and functional evolution of several of the most important copper mining, milling, and smelting districts of Michigan’s Keweenaw Peninsula.

This research discovered that CityEngine, while intended for rapid 3D modeling of the contemporary urban landscape, was sufficiently robust and flexible to be applied to modeling serial historic industrial landscapes. While this novel application required some additional coding and finish work, by harnessing this software to existing big data sets, 48,000 individual buildings were rapidly visualized using several key variables.

This paper presents a new and useful application of an existing 3D modeling software, helping to further illuminate and inform the management and conservation of the rich heritage of this still-evolving postindustrial landscape.

As the demand for online auctions increases, the process of monitoring multiple auction houses, deciding which auction to participate in and making the right bids, become challenging tasks for consumers. Hence, knowing the closing price of a given auction would be an advantage, since this information will ensure a win in a given auction. However, predicting a closing price for an auction is not easy, since it is dependent on many factors. The purpose of this paper is to report on a predictor agent that utilises grey system theory to predict the closing price for a given auction.

The focus of the research is on grey system agent. This paper reports on the development of a predictor agent that attempts to predict the online auction closing price in order to maximise the bidder's profit. The performance of this predictor agent is compared with two well‐known techniques, the Simple Exponential Function and the Time Series, in a simulated auction environment and in the eBay auction.

The grey theory agent gives a better result when less input data are made, while the Time Series Agent can be used with the availability of a lot of information. Although the Simple Exponential Function Agent is able to predict well with less input data, it is not an appropriate method to be applied in the prediction model since its formula is not realistic and applicable in predicting the online auction closing price. The experimental results also showed that using moving historical data produces a higher accuracy rate than using fixed historical data for all three agents.

Grey system theory prediction model, GM(1, 1) has not been applied in online auction prediction. In this paper the authors have applied grey theory into an agent to predict the closing price of an online auction, in order to increase the profit of bidders in the bidding stage. The experimental results show that the accuracy of the grey prediction model is more then 90 per cent, with less then eight historical data inputs.

While quantification and performance measurement have proliferated widely in academia and the business world, management and organization scholars increasingly agree on the need for a more in-depth focus on the complex dynamics embedded in the construction, use and effects of quantitative measures (pertaining to the thread of research called ethnostatistics). This paper develops a pluralistic method for conducting ethnostatistical research in organizational settings. Whilst presenting practical techniques for conducting research in live settings, it also discusses how historical approaches which focus on source criticism and contextual reconstruction could overcome the limitations of ethnostatistics.

The methodological approach of this paper encompasses an in-depth discussion of the ethnostatistical method, its underlying assumptions and its methodological limitations. Based on this analysis, the authors propose a pluralistic method (model) for conducting ethnostatistical research in organizational settings based on the integration of 1) research practices employed by one of the authors conducting ethnostatistical research in a large multinational organization and 2) best practices from ethnographic and historical research.

This paper suggests how historical approaches can successfully join ethnostatistical enquiries in an attempt to overcome some limitations in existing conventional methods. The developed framework explores four levels of analysis (ethnography, statistics at work, rhetoric of statistics and history of statistics) and suggests practical approaches for each level that can contribute to strengthening the research output and overcoming limitations when using ethnostatistics.

This paper contributes to the ethnostatistical field by discussing the intersection between history and ethnography and the ways for their complementary use in organizational and management research on quantification processes. As such it offers unique insights and hands-on experience from conducting ethnostatistical enquiries in live organizational settings.

The National Archives (TNA) is the UK Government's official archive. It stores and maintains records spanning over a 1,000 years in both physical and digital form. Much of the information held by TNA includes references to place and frequently user queries to TNA's online catalogue involve searches for location. The purpose of this paper is to illustrate how TNA have extracted the geographic references in their historic data to improve access to the archives.

To be able to quickly enhance the existing archival data with geographic information, existing technologies from Natural Language Processing (NLP) and Geographical Information Retrieval (GIR) have been utilised and adapted to historical archives.

Enhancing the archival records with geographic information has enabled TNA to quickly develop a number of case studies highlighting how geographic information can improve access to large‐scale archival collections. The use of existing methods from the GIR domain and technologies, such as OpenLayers, enabled one to quickly implement this process in a way that is easily transferable to other institutions.

The methods and technologies described in this paper can be adapted, by other archives, to similarly enhance access to their historic data. Also the data‐sharing methods described can be used to enable the integration of knowledge held at different archival institutions.

Place is one of the core dimensions for TNA's archival data. Many of the records which are held make reference to place data (wills, legislation, court cases), and approximately one fifth of users' searches involve place names. However, there are still a number of open questions regarding the adaptation of existing GIR methods to the history domain. This paper presents an overview over available GIR methods and the challenges in applying them to historical data.

The rich primary and secondary data sources for studying historical Chinese marketing theory and practice are discussed. This paper aims to briefly address possible challenges (and their solutions) to using these sources.

A bibliographic review is used to analyze historical sources pertaining to Chinese marketing theory and practice.

Marketing scholars can draw from multiple but neglected and underused Chinese sources to glean important historical data reflecting pre-1949 Chinese marketing.

Underused Chinese multilateral historical marketing materials are inalienable to extending historical marketing study. Many studies about marketing theory and practice are amenable to such materials.

By scrutinizing these materials, contemporary marketers can formulate parallel strategies from the repertoire of historical marketing strategies.

This is the first comprehensive survey of an invaluable non-Western source for historical research in marketing.

The purpose of this paper is to adopt an assemblage theory lens to examine the socio-material forces shaping the development of an infrastructure for the recovery of archived historical marine weather records for use in contemporary climate data sets.

The authors adopted a data journeys approach to research design, conducting in-depth semi-structured interviews with climate scientists, citizen scientists and a climate historian who were engaged at key sites across the journey of data from historical record to the International Comprehensive Ocean-Atmosphere Data Set database. Interview data were complemented by further qualitative data collected via observations of working practices, a digital ethnography of citizen scientists’ online forums, and documentation relevant to the circulation and governance of climate data across emergent data infrastructures. Data were thematically analysed (Ryan and Bernard, 2003), with themes being informed primarily by the theoretical framework.

The authors identify and critically examine key points of friction in the constitution of the data recovery infrastructure and the circulation of data through it, and identify the reflexive and adaptive nature of the beliefs and practices fostered by influential actors within the assemblage in order to progress efforts to build an infrastructure despite significant challenges. The authors conclude by addressing possible limitations of some of these adaptive practices within the context of the early twenty-first century neoliberal state, and in light of current debates about data justice.

The paper draws upon original empirical data and a novel theoretical framework that draws together Deleuze and Guattari’s assemblage theory with key concepts from the field of critical data studies (data journeys, data friction and data assemblage) to illuminate the socio-material constitution of the data recovery infrastructure within the context of the early twenty-first century neoliberal state.

One of the most serious problems faced by the multinational retailer is the estimation of the structure of retail markets in the many countries that he might want to enter. While marketing scholars have developed methods for assessing market potentials and for evaluating economic and political conditions, they have not been as successful in solving the market entry problem as it relates to the development of retailing. There are a large number of models of retail change which can be applied to the process, however, they generally fall short of management desires. In the first place, these models are often based on hypotheses of retail change in the North American market. In the second place, many of the models have not been formally tested within that environment and few have been evaluated in the wider context. Finally, these models are not comparative and the use of them in comparative analysis would suffer from the absence of acceptable categories and data. One approach that might help the difficulties of market entry is to apply historical research methods to the problem of understanding change. These methods by their very nature embody the elements of comparative analysis and offer the added advantage of dealing with specific firms and events rather than concentrating on more aggregate categories. By focusing on specific retailers in a single country and across many countries the researcher can develop general propositions which are deductively arrived at in a systematic fashion rather than rely on chance observation of aggregate structure and behaviour. Historical research demands a new outlook and sensitivity in the observation of events and in the analysis of data. The skills required to undertake such research have direct benefits because they force the researcher clearly to define the elements of his study in a way not possible when statistical methods are employed. Will such research be the answer to all of the issues of multinational retailing? The answer is clearly no; however, it can help managers and marketing scholars better understand the process of change in the past. There is no certainty that the past will predict the future, but understanding the process of change might make the future more manageable; and, what better place is there to start than a historical perspective?