The platform for open and practice-oriented research

product

Data-driven work in the creative & media industries

The report from Inholland University is dedicated to the impacts of data-driven practices on non-journalistic media production and creative industries. It explores trends, showcases advancements, and highlights opportunities and threats in this dynamic landscape. Examining various stakeholders' perspectives provides actionable insights for navigating challenges and leveraging opportunities. Through curated showcases and analyses, the report underscores the transformative potential of data-driven work while addressing concerns such as copyright issues and AI's role in replacing human artists. The findings culminate in a comprehensive overview that guides informed decision-making in the creative industry.

MULTIFILE

Data-driven work in the creative & media industries

product

Lives of data

Lives of Data maps the historical and emergent dynamics of big data, computing, and society in India. Data infrastructures are now more global than ever before. In much of the world, new sociotechnical possibilities of big data and artificial intelligence are unfolding under the long shadows cast by infra/structural inequalities, colonialism, modernization, and national sovereignty. This book offers critical vantage points for looking at big data and its shadows, as they play out in uneven encounters of machinic and cultural relationalities of data in India’s socio-politically disparate and diverse contexts.Lives of Data emerged from research projects and workshops at the Sarai programme, Centre for the Study of Developing Societies. It brings together fifteen interdisciplinary scholars and practitioners to set up a collaborative research agenda on computational cultures. The essays offer wide-ranging analyses of media and techno-scientific trajectories of data analytics, disruptive formations of digital economy, and the grounded practices of data-driven governance in India. Encompassing history, anthropology, science and technology studies (STS), media studies, civic technology, data science, digital humanities, and journalism, the essays open up possibilities for a truly situated global and sociotechnically specific understanding of the many lives of data.

MULTIFILE

product

Developing Data Driven Business Models for Interactive Media Companies

The growing availability of data offers plenty of opportunities for data driven innovation of business models for SMEs like interactive media companies. However, SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data-driven business models. In this paper we develop and evaluate a practical method for designing data driven business models (DDBM) in the context of interactive media companies. The development follows a design science research approach. The main result is a step-by-step approach for designing DDBM, supported by pattern cards and game boards. Steps consider required data sources and data activities, actors and value network, revenue model and implementation aspects. Preliminary evaluation shows that the method works as a discussion tool to uncover assumptions and make assessments to create a substantiated data driven business model.

MULTIFILE

Developing Data Driven Business Models for Interactive Media Companies

product

Data-driven visibility: maternal bodies

Through artistic interventions into the computational backbone of maternity services, the artists behind the Body Recovery Unit explore data production and its usages in healthcare governance. Taking their artwork The National Catalogue Of Savings Opportunities. Maternity, Volume 1: London (2017) as a case study, they explore how artists working with ‘live’ computational culture might draw from critical theory, Science and Technology Studies as well as feminist strategies within arts-led enquiry. This paper examines the mechanisms through which maternal bodies are rendered visible or invisible to managerial scrutiny, by exploring the interlocking elements of commissioning structures, nationwide information standards and databases in tandem with everyday maternity healthcare practices on the wards in the UK. The work provides a new context to understand how re-prioritisation of ‘natural’ and ‘normal’ births, breastfeeding, skin-to-skin contact, age of conception and other factors are gaining momentum in sync with cost-reduction initiatives, funding cuts and privatisation of healthcare services.

MULTIFILE

product

Times Series Building Energy Systems data Imputation

Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.

PDF

Times Series Building Energy Systems data Imputation

product

Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.

PDF

Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

product

Citizens’ data‑ing with contemporary data in their daily life

Citizens regularly search the Web to make informed decisions on daily life questions, like online purchases, but how they reason with the results is unknown. This reasoning involves engaging with data in ways that require statistical literacy, which is crucial for navigating contemporary data. However, many adults struggle to critically evaluate and interpret such data and make data-informed decisions. Existing literature provides limited insight into how citizens engage with web-sourced information. We investigated: How do adults reason statistically with web-search results to answer daily life questions? In this case study, we observed and interviewed three vocationally educated adults searching for products or mortgages. Unlike data producers, consumers handle pre-existing, often ambiguous data with unclear populations and no single dataset. Participants encountered unstructured (web links) and structured data (prices). We analysed their reasoning and the process of preparing data, which is part of data-ing. Key data-ing actions included judging relevance and trustworthiness of the data and using proxy variables when relevant data were missing (e.g., price for product quality). Participants’ statistical reasoning was mainly informal. For example, they reasoned about association but did not calculate a measure of it, nor assess underlying distributions. This study theoretically contributes to understanding data-ing and why contemporary data may necessitate updating the investigative cycle. As current education focuses mainly on producers’ tasks, we advocate including consumers’ tasks by using authentic contexts (e.g., music, environment, deferred payment) to promote data exploration, informal statistical reasoning, and critical web-search skills—including selecting and filtering information, identifying bias, and evaluating sources.

LINK

product

Data science in government agencies

Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.

PDF

product

The relationship between the nomophobic levels of higher education students in Ghana and academic achievement

There is an upsurge in the use of mobile phones among higher education students in Ghana, which may result in the nomophobia prevalence with the students. Therefore, the need to assess the influence of nomophobia within the student population in Ghana. This descriptive cross-sectional study investigated the prevalence of nomophobia and the sociodemographic variables, and the association with academic achievement of the understudied population. A self-reporting nomophobia questionnaire, composed of 20 dimensions, was answered by 670 university students to measure the nomophobia prevalence. Raw data were estimated using descriptive statistics, and one-way ANOVA and Independent T-test. While the findings showed diverse grades of nomophobia, statistical significance between academic achievement and the level of nomophobia was observed. This study concludes that there is a high nomophobia prevalence among university students in Ghana as the use of smartphones increases. However, follow-up studies should be conducted in Ghanaian universities to monitor nomophobia and its associates in order to reduce the adverse effects of habitual use of smartphones.

PDF

Search results

Products 673

Data-driven work in the creative & media industries

Lives of data

Developing Data Driven Business Models for Interactive Media Companies

Data-driven visibility: maternal bodies

Times Series Building Energy Systems data Imputation

Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

Citizens’ data‑ing with contemporary data in their daily life

Data science in government agencies

The relationship between the nomophobic levels of higher education students in Ghana and academic achievement

Navigate to

Categories

Filters

Products 673

Data-driven work in the creative & media industries

Lives of data

Developing Data Driven Business Models for Interactive Media Companies

Data-driven visibility: maternal bodies

Times Series Building Energy Systems data Imputation

Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

Citizens’ data‑ing with contemporary data in their daily life

Data science in government agencies

The relationship between the nomophobic levels of higher education students in Ghana and academic achievement