Sound data intensive science depends upon effective research data and information management. Efficient and interoperable research information systems will be crucial for enabling and exploiting data intensive research however it is equally important that a research ecosystem is cultivated within research-intensive institutions that foster sustainable communication, cooperation and support of a diverse range of research-related staff. Researchers, librarians, administrators, ethics advisors, and IT professionals all have a vital contribution to make in ensuring that research data and related information is available, visible, understandable and usable over the mid to long term. This paper will provide a summary of several ongoing initiatives that the Jisc-funded Digital Curation Centre (DCC) are currently involved with in the UK and internationally to help staff within higher education institutions prepare to meet funding body mandates relating to research data management and sharing and to engage fully in the digital agenda.

The Danish Ministry of Culture has funded a project to set up a model for costing preservation of digital materials held by national cultural heritage institutions. The overall objective of the project was to increase cost effectiveness of digital preservation activities and to provide a basis for comparing and estimating future cost requirements for digital preservation. In this study we describe an activity-based costing methodology for digital preservation based on the Open Archice Information System (OAIS) Reference Model. Within this framework, which we denote the Cost Model for Digital Preservation (CMDP), the focus is on costing the functional entity Preservation Planning from the OAIS and digital migration activities. In order to estimate these costs we have identified cost-critical activities by analysing the functions in the OAIS model and the flows between them. The analysis has been supplemented with findings from the literature, and our own knowledge and experience. The identified cost-critical activities have subsequently been deconstructed into measurable components, cost dependencies have been examined, and the resulting equations expressed in a spreadsheet. Currently the model can calculate the cost of different migration scenarios for a series of preservation formats for text, images, sound, video, geodata, and spreadsheets. In order to verify the model it has been tested on cost data from two different migration projects at the Danish National Archives (DNA). The study found that the OAIS model provides a sound overall framework for the cost breakdown, but that some functions need additional detailing in order to cost activities accurately. Running the two sets of empirical data showed among other things that the model underestimates the cost of manpower-intensive migration projects, while it reinstates an often underestimated cost, which is the cost of developing migration software. The model has proven useful for estimating the costs of preservation planning and digital migrations. However, more work is needed to refine the existing equations and include the other functional entities of the OAIS model. Also the user-friendliness of the spreadsheet tool must be improved in future versions of the model. The CMDP is presently closing its second phase, where it has been extended to include the OAIS Functional Entity Ingest. This has also enabled us to adjust the theoretical model further, especially regarding the accuracy and precision of the model and in relation to the underlying parameters used in the equations, such as migration frequency and format complexity. Understanding the nature of digital preservation cost is prerequisite for increasing the overall efficiency, and achieving first quality for preservation of cultural heritage materials.

Control of temperature and relative humidity in storage areas and exhibitions is crucial for long-term preservation of cultural heritage objects. This paper explores the possibilities for developing a proactive system, based on a machine-learning model (XGBoost), for predicting the occurrence of unwanted indoor environmental conditions: either a too high or a too low relative humidity, within the forthcoming 24 h. The features used in the model were hourly indoor and outdoor climate recordings, and it was applied to two indoor heritage environments; a storage facility and a church building. The test accuracy (f1-score) of the model was good (0.93 for high RH; 0.93 for low RH) when applied to the storage building, but only 0.78; 0.62 (high RH; low RH) for the church building test. Challenges encountered include difficulties in obtaining good historical climate data sets for training and testing the model, and the dependency of external IT systems, which, if they fail, inactivates the model without a warning. Several issues call for more research: A desirable improvement of the model would be predictions for periods longer than 24 h ahead, still maintaining a high test accuracy. Further perspectives of using machine learning for indoor environmental forecasting could be for indoor air pollution, or energy consumption due to climate control.

There are a variety of image quality analysis tools available for the archiving world, which are based on different test charts and analysis algorithms. ISO has formed a working group in 2012 to harmonize these approaches and create a standard way of analyzing the image quality for archiving systems. This has resulted in three documents that have been or are going to be published soon. ISO 19262 defines the terms used in the area of image capture to unify the language. ISO 19263 describes the workflow issues and provides detailed information on how the measurements are done. Last but not least ISO 19264 describes the measurements in detail and provides aims and tolerance levels for the different aspects. This paper will present the new ISO 19264 technical specification to analyze image quality based on a single capture of a multi-pattern test chart, and discuss the reasoning behind its current design.