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Physical science depends on historical records as well as new ideas, results, and opportunities. In this increasingly digital era, in which much of modern day life is technology-driven, a significant amount of scientific data remains in forms that are inaccessible to current methods. Moreover, the creation and subsequent accessing of high quality reusable data continues to present challenges. As part of the pilot phase of the Physical Sciences Data Infrastructure (PSDI) Initiative we investigated the current landscape of digitisation in 2022, aiming to produce an outline of what the physical sciences community requires from a Digital Research Environment (DRE). Evidence suggests that while scientists are digitising diverse portions of their work, some research still remains lost in paper lab books, and many barriers (hardware, software and people) persist for the effective management of scientific data. Our studies build on previous research and include an informal survey of what the UK Physical Sciences community considered to be the key requirements for capturing, sharing, and accessing data. The paper ends by outlining future prospects for PSDI and provides some concrete actions for different stakeholders in the community.

There are a number of issues that inhibit the replication and reproduction of research, and make it hard to utilise existing scientific data to make new discoveries. These include poor data management, competing standards, a lack of consideration of the usability of data, and a disconnect between the publication of science and the data and methods behind it. In this paper, we examine the benefits of good data management for not only ensuring that data are well organised, easy to find, and preserved for the future, but also for facilitating reproducibility and new discoveries in science. We consider the importance of documenting data and making them usable by both humans and machines, and consider the development of tools to support these processes in the future.

As the volume of data associated with scientific research has exploded over recent years, the use of digital infrastructures to support this research and the data underpinning it has increased significantly. Physical chemists have been making use of eScience infrastructures since their conception, but in the last five years their usage has increased even more. While these infrastructures have not greatly affected the chemistry itself, they have in some cases had a significant impact on how the research is undertaken. The combination of the human effort of collaboration to create open source software tools and semantic resources, the increased availability of hardware for the laboratories, and the range of data management tools available has made the life of a physical chemist significantly easier. This review considers the different aspects of eScience infrastructures and explores how they have improved the way in which we can conduct physical chemistry research.

In this chapter, we will take a look back at the various issues discussed in the book and attempt to bring together the various threads to consider some of the unresolved questions that may change the way that science is recorded in the future. Although the issues may seem unrelated initially, they may in fact converge when we think about the potential role of digital tools in capturing and making use of the scientific record. First, we will take a look at what belongs in the scientific record and expand upon our definition from Chapter 1. We will take a look at the nature of Electronic Laboratory Notebooks (ELNs) and consider whether there is a need to development digital research management platforms or ELN-type tools that can do everything to support the needs of researchers. Next, we will examine the arguments for and against paper, and consider whether the move to paperless technology is essential for progress in science. Finally, we will consider storytelling and serendipity in science and different ways that stories about research and researchers might be told using digital tools and other methods.

Electronic Laboratory Notebooks (ELNs) have the potential to not only replace paper notebooks for recording our research and our day-to-day activities, but they can also provide a myriad of additional capabilities for supporting scientists that go far beyond what is possible in a paper notebook. However, we know that many scientists are sceptical of the technology and have anxieties about the complexity of the software, and perceived ease of use is of primary importance to users in selecting an ELN provider. It is essential to ensure that usability is at the forefront in the design of digital tools to encourage adoption and also to ensure that the users get the best value out of the functions that are provided. The design of interfaces for digital tools not only impacts whether users are willing to use them, but also whether users are able to make the best use of them. In this chapter, we discuss a variety of user experience design techniques, including interaction design, that are used to help make software both useful and usable for the intended audience. We will investigate how these techniques can be used to identify requirements for new tools based on user needs and present some examples of potential ELN capabilities based on the needs and behaviours of real-world researchers. This chapter also includes guidelines and best practices for designing interfaces for desktop, mobile, and Web-based software.

Electronic Laboratory Notebooks (ELNs) have many features that can support the work of scientists, but their main purpose is to replace the paper notebook in the laboratory. When we capture notes in a paper notebook, we have the flexibility to record our research in any format we choose. When we capture notes using an electronic notebook, and particularly using ELNs, the way that we capture information changes. Many ELNs make use of templates to encourage the capture of standard information and provide the opportunity to prompt users to capture items of interest that they might otherwise omit or forget to record. Using the cues provided by the formal structure, templates should improve the consistency and quality of records. There is, however, the possibility that templates may have negative as well as positive impacts because they may restrict the content that is recorded, and information that is not specifically prompted for in the template may be omitted, even if it is significant to the record. In this chapter, we will examine research into the impacts of different kinds of interfaces on the information that is captured in the laboratory. We also take a look at the role of mobile applications in bridging the gap between paper notebooks and ELNs, and how other kinds of interfaces may better support users in capturing their research in electronic form.

Technology provides numerous ways that scientists can share their information and interact with others. The Internet in particular has driven changes in the way that scientists work, promoting better access to publications and data, and more opportunities for sharing and collaboration. In this chapter, we will discuss the various ‘open’ initiatives that facilitate Open Science. We will also look at how Open Science can facilitate both collaboration across the scientific community and public engagement with science. We will also discuss how open access to data can provide opportunities to encourage mutually beneficial collaboration between volunteers and scientists through citizen science. We will explore the barriers to Open Science and look at some of the ways these barriers can be overcome. In particular, we will take a look at the role of recognition in sharing and how different methods of providing recognition, such as altmetrics, might encourage the sharing of research data. Finally, we will take a look at digital tools in Open Science, including those for sharing data, replicating research, and communicating scientific knowledge.

In this book, we have explored how researchers record their research and how current digital technologies can facilitate the capture and organisation of research data and the research record. In this chapter, we take a look at how newer technologies such as Virtual Reality, Augmented Reality, and Artificial Intelligence can provide new ways for researchers to make use of and interact with their own scientific data and scientific data created by others. We examine the role of the Internet of Things in not only capturing data through the use of sensors and connected systems, but also how such devices can be used to interact with data and capture scientific activities. We also look at how wearables and smartphones can be used in science. Technology can make our lives simpler or more complicated, and more natural ways of interacting with computers may make the technology simpler and more intuitive to use. In this chapter, we will also look at a variety of ‘natural interfaces’ with some examples of how they have been used in applications for science. Finally, we will take a look at how the technologies might be brought together to provide smart environments and intelligent assistants to facilitate the everyday activities of scientists and helping to provide alternate ways of capturing the scientific record.

As more and more scientific data is being created in digital formats, the need to be able to link together the data with the methods used to obtain it becomes ever more urgent. Digital data without the context provided by the notes we typically collect in our paper notebooks is meaningless. Electronic notebooks have the potential to revolutionise the way that scientists record their research. Not only can electronic notebooks enable us to create better links between the research activities recorded and the data they produce, but they are also easier to read, easier to search, enable sharing and collaboration, and can improve quality and efficiency in working practices. Despite the benefits, the majority of scientists are still relying on their trusty paper notebooks to capture the record of their research. In this chapter, we take a more in-depth look at electronic notebooks, and Electronic Laboratory Notebooks (ELNs) in particular. We take a look at the numerous benefits of electronic notebooks over paper notebooks and discuss a number of different ELNs to provide examples of differences and overlaps in their capabilities and target audience. We also look at some experiences of using an ELN from different disciplines, and how an ELN can facilitate and support scientific research in different ways. We also take a look at what some of the potential barriers are to the adoption of ELNs and why some scientists are reluctant to use them.

The development of computer technology has had a huge impact on the way that scientists work, providing tools not only for everyday tasks such as report writing and email, but also tools that facilitate new ways of collecting and recording data. This chapter provides a brief history of the transformation from analogue to digital in science, including the digitisation of instruments and the development of technologies for storing and managing data. We will take an in-depth look into the development and role of digital tools for record-keeping, in particular the use of electronic notebooks in both the field and laboratory. We will also take a brief look at the impact of computational science and working in silico.

The scientific record is comprised of both the published results of scientific activities in the form of journal articles, books, and reports, but equally significant are the day-to-day records kept by researchers. In this chapter, we look at the ‘inscription’ behaviours of scientists and the development of record-keeping in the sciences. We also investigate the principle motivations for taking notes in science, who uses them, and their importance for enabling trust in science by facilitating reproducibility. In this chapter, we also take an in-depth look at the role of memory in note-taking. Understanding how memory works can inform how we design digital tools to support scientists and better capture the scientific record for the future. We explore how knowledge is stored in our minds and what influences our ability to remember and recall that knowledge. We also look at how different factors can change the information in our memories and what we recall when prompted.

Effective data management and curation are essential to not only ensure that our research data is discoverable and safely preserved, but also so that it is reusable and repeatable by others. Rich metadata can be used to describe the data, provide context, and capture provenance to facilitate discoverability, reusability, and trust in the research data. In this chapter, we will discuss the roles of both machine-generated and user-defined metadata and the importance of capturing metadata ‘at source’. We will also examine how metadata is used in science, and issues and attitudes towards creating metadata. Software can assist users in the production of metadata and by making use of metadata within interfaces, and we will examine some ways that metadata can be automatically created and examples of smart assistance in digital tools. We will look at ways of encouraging curation through the design of interfaces, including how using invitations, different cues, and changing perspectives can help to capture different kinds of metadata. Finally, we will examine existing digital tools for metadata design and creation, and how including usability in tool design can support users with such tools.

In this chapter, we investigate various aspects of record-keeping behaviour. These include the life-cycle of the scientific record and how the high-level processes of scientific research are used to construct it. We also look at how researchers learn to keep records and what influences the structure, content, and organisation of their notebooks, together with some examples from a variety of scientific disciplines. Keeping notes is more than just making random jottings in the notebook and we will discuss how the personal and private nature of notebooks, and the thoughts and experiences captured within, can influence the sharing behaviours of researchers.

Until relatively recently, scientific data has mostly been captured in paper notebooks or on paper forms. With the proliferation of digital technologies has come an even greater proliferation in the amount of scientific data produced and that subsequently needs to be managed. Although there are some challenges in managing data in paper-based form, there are significantly larger challenges in managing data in both paper and digital format. There is also an increasing desire for scientific data to be shared more widely in order that research can be validated and data reused. Both the creators and users of scientific data need to be able to find the data and then make sense of it in order to use it. In this chapter, we will discuss the issues and challenges around research reproducibility, and how data management practices can help. We discuss the role of metadata, standards, identifiers, and provenance in data curation and their importance for discoverability and reuse. We also look at the vulnerability of digital data and the steps that need to be taken to ensure that data is preserved for future access and reuse.

The Southampton chemical information group had its genesis in 2001, when we began an e-Science pilot project to investigate structure-property mapping, combinatorial chemistry, and the Grid. CombeChem instigated a range of activities that have since been underway for more than ten years, in many ways matching the expansion of interest in using the Web as a vehicle for collection, curation, dissemination, reuse, and exploitation of scientific data and information. Chemistry has frequently provided the exemplar case studies, notably for the series of projects – funded by Jisc and EPSRC – that investigated the issues associated with the long-term preservation of data to support the scholarly knowledge cycle, such as the eBank UK project. Rapid developments in Internet access and mobile technology have significantly influenced the way researchers view connectivity, data standards, and the increasing importance and power of semantics and the Semantic Web. These technical advances interact strongly with the social dimension and have led to a reconsideration of the responsibilities of researchers for the quality of their research and for satisfying the requirements of modern stakeholders. Such obligations have given rise to discussions about Open Access and Open Data, creating a range of alternatives that are now technically feasible but need to be socially acceptable. Business plans are changing too, but in a strange contradiction, desire can run ahead of what is possible, sensible, and affordable, while lagging behind in imagination of what would be technically possible and potentially game-changing! Taking the chemical sciences as our example and focusing on the curation of research data, we explore from our perspective, ten years back and ten years forward, how far we have been able to re-imagine the data/information value pathway from bench to publication. We assess not only the major advances and changes that have been achieved, but also where we have been less successful than we might have hoped. We explore the directions for the future, based on what is clearly already possible and on what we can envisage becoming feasible in the near future.

User-defined metadata is useful for curating and helping to provide context for experiment records, but our previous investigations have demonstrated that simply providing the facility to add metadata is not enough to ensure that metadata is added, let alone to ensure that the metadata is of high quality. For metadata to be useful it first has to be present, but enforcing metadata generation is of no benefit if it is low quality, inconsistent, or irrelevant. Researchers need support. One strategy to encourage more effective metadata creation is to design user interfaces that invite users to add metadata by asking them questions. If we ask users specific questions about their experiments and other activities then we could capture more relevant or useful metadata, although there is a risk that asking the wrong questions may lead to loss of valuable metadata terms or the creation of irrelevant material. In this paper we report on a study to investigate how different questions could be used to generate metadata by eliciting information in three different conditions: free recall, changing perspective by thinking about search terms to help someone else, and providing cues by using a set of topic-based questions. We also investigate how responses varied with different information types. The results of the study show that different terms are created under the different conditions, as expected. The use of cues generates the highest numbers of terms and the most diverse range, including elements that are not captured in other conditions. However, important themes generated in other conditions are not produced because the cues to create them are missing. The study also generated a number of unexpected findings, including responses describing information that is not in the original material: personal opinions and experiences, and comments about the information text itself. These unexpected responses have both positive and negative consequences for the generation of metadata and the curation of scientific records. The results of studies using these techniques to capture metadata for chemistry experiments are also discussed.

Chemical scientists’ experiences with LabTrove., Electronic Laboratory Notebooks (ELNs) are progressively replacing traditional paper books in both commercial research establishments and academic institutions. University researchers require specific features from ELNs, given the need to promote cross-institutional collaborative working, to enable the sharing of procedures and results, and to facilitate publication. The LabTrove ELN, which we use as our exemplar, was designed to be researcher-centric (i.e., not only aimed at the individual researcher's basic needs rather than to a specific institutional or subject or disciplinary agenda, but also able to be tailored because it is open source). LabTrove is being used in a heterogeneous set of academic laboratories, for a range of purposes, including analytical chemistry, X-ray studies, drug discovery and a biomaterials project. Researchers use the ELN for recording experiments, preserving data collected, and for project coordination. This perspective article describes the experiences of those researchers from several viewpoints, demonstrating how a web-based open source electronic notebook can meet the diverse needs of academic researchers.

The drive toward more transparency in research, the growing willingness to make data openly available, and the reuse of data to maximize the return on research investment all increase the importance of being able to find information and make links to the underlying data. The use of metadata in Electronic Laboratory Notebooks (ELNs) to curate experiment data is an essential ingredient for facilitating discovery. The University of Southampton has developed a Web browser-based ELN that enables users to add their own metadata to notebook entries. A survey of these notebooks was completed to assess user behavior and patterns of metadata usage within ELNs, while user perceptions and expectations were gathered through interviews and user-testing activities within the community. The findings indicate that while some groups are comfortable with metadata and are able to design a metadata structure that works effectively, many users are making little attempts to use it, thereby endangering their ability to recover data in the future. A survey of patterns of metadata use in these notebooks, together with feedback from the user community, indicated that while a few groups are comfortable with metadata and are able to design a metadata structure that works effectively, many users adopt a "minimum required" approach to metadata. To investigate whether the patterns of metadata use in LabTrove were unusual, a series of surveys were undertaken to investigate metadata usage in a variety of platforms supporting user-defined metadata. These surveys also provided the opportunity to investigate whether interface designs in these other environments might inform strategies for encouraging metadata creation and more effective use of metadata in LabTrove.

Egyptian evidence of scientific records dates back almost 50 centuries. In more recent times da Vinci and Faraday provide role models for scrupulous recording of ideas, observations, and conclusions. Their medium was paper, but despite the quality of their notebooks, we cannot turn the clock back. Our primary purpose is to review the influences of the digital era on scientific record keeping. We examine the foundations of the emerging opportunities for preserving and curating electronic records focussing on electronic laboratory notebooks (ELNs), with an emphasis on their characteristics and usability.

During May and June 2011, we ran two workshops with a theme entitled “Smart Spaces for Smart People”. Although organized under the auspices of the e-Science Institute, the participants came from a variety of disciplines and brought a range interests. We placed a strong emphasis on facilitated discussion, with the clear intention to explore ideas about exploiting the interactions that could occur within smart spaces. Although the workshops formulated the view that no space is, or can be, inherently smart, we deemed certain components to be essential for a system to achieve smartness, most notably people; the role of hardware and software technologies is to confer capability. The lessons we learned are applicable to any smart meeting. We grouped our findings under four sub-themes that we identified as a basis for the successful planning and conduct of smart meetings. After examining the principal ideas associated with each sub-theme, we go on to consider how these ideas might influence strategies for exploiting smart meetings for knowledge transfer. This paper is the third in a series of three, each dealing with different aspects of the workshops and how they influenced our thinking about knowledge transfer meetings, particularly in the context of sharing research outputs.

During May and June 2011, we ran two workshops with a theme entitled “Smart Spaces for Smart People” [1]. Although organized under the auspices of the e-Science Institute, the participants came from a variety of disciplines and brought a range interests. The workshops themselves were run as experiments in running smart meetings with the intentions of exchanging and recording knowledge and decisions discussed in the meeting. A recurring theme in the workshops was not only that technology can be provided in a smart space to help in the knowledge transfer and recording process, but also that the technology will only be adopted and exploited if the users of the smart space can easily use it. There are other human factors that affect the success of collaboration in a smart space. These include the willingness for participates to collaborate if they have concerns over privacy and anonymity, particularly when discussions and decisions are recorded using technology. The dynamics of how participants work together in groups to transfer knowledge can also be enhanced through the use of smart spaces. The fact that the workshops were run in different physical environments also provided insights into how the physical design of the meeting space might have on effective collaboration and therefore effective transmission of knowledge. This paper is the second in a series of three, each dealing with different aspects of the workshops and how they influenced our thinking about knowledge transfer meetings, particularly in the context of sharing research outputs.

Given the current emphasis on the diligent management of research data, it is almost self-evident that curation should be a fundamental aspect of the research practice in the chemical sciences. An essential ingredient in the curation process is metadata: descriptive information and classification labels that group related items, provide context and facilitate the reuse of specified research outputs. In our view, the single most important function of metadata is to capture context. Problems can and do arise later in the research cycle if researchers do not capture the correct context as they record their experiments and acquire their data, so curation is naturally most effective when researchers capture metadata at source. However, researchers perceive curation as a burden, raising a range of issues that we examine generically and with regard specifically to the challenges that the burden of creation presents in the chemical sciences. We examine the significance of training in data management and explore the potential role for librarians. We also consider the issues relating to the deployment of controlled vocabularies. We conclude with the view that chemists have yet to take full advantage of the extra opportunities created by effective curation, emphasising the importance of capturing contextual information at the earliest possible stage: at source.