In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of "dynamic data citation". The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution.

This master thesis aims to analyse the data curation situation and the decision-making processes of the archivists at the Swedish Peace Archives. With the theories in data curation and methodology in analysing both accessible web content and interviews, this thesis hopes to shed light on the institutional perspective in managing digital archives. By studying and analysing the empirical materials from the online archive websites and the database, the data curation theories are applied to locate the archive practices at different levels. The method in collecting qualitative data from interviews with the archivists gives insight into archive works and digitisation processes in the digital archives today.  The results show the complexity in data management from digitisation to curation, and digital archives as an information-intensive digital environment has caused the merge of humanities and archives scholarship. This shows the limitation in the data-centric digital humanities frameworks when discussing archive management and technical interoperability. A further study with more focus on individual institutions with their unique workflows, stakeholders, and material types in mind is therefore suggested.

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L'émergence de nouvelles plateformes décentralisées pour la création de données, tel que les plateformes mobiles, les capteurs et l'augmentation de la disponibilité d'open data sur le Web, s'ajoute à l'augmentation du nombre de sources de données disponibles et apporte des données massives sans précédent à être explorées. La notion de curation de données qui a émergé se réfère à la maintenance des collections de données, à la préparation et à l'intégration d'ensembles de données (data set), les combinant avec une plateforme analytique. La tâche de curation inclut l'extraction de métadonnées implicites et explicites ; faire la correspondance et l'enrichissement des métadonnées sémantiques afin d'améliorer la qualité des données. La prochaine génération de moteurs de gestion de données devrait promouvoir des techniques avec une nouvelle philosophie pour faire face au déluge des données. Ils devraient aider les utilisateurs à comprendre le contenue des collections de données et à apporter une direction pour explorer les données. Un scientifique peut explorer les collections de données pas à pas, puis s'arrêter quand le contenu et la qualité atteignent des niveaux satisfaisants. Notre travail adopte cette philosophie et la principale contribution est une approche de curation des données et un environnement d'exploration que nous avons appelé CURARE. CURARE est un système à base de services pour curer et explorer des données volumineuses sur les aspects variété et variabilité. CURARE implémente un modèle de collection de données, que nous proposons, visant représenter le contenu structurel des collections des données et les métadonnées statistiques. Le modèle de collection de données est organisé sous le concept de vue et celle-ci est une structure de données qui pourvoit une perspective agrégée du contenu des collections des données et de ses parutions (releases) associées. CURARE pourvoit des outils pour explorer (interroger) des métadonnées et pour extraire des vues en utilisant des méthodes analytiques. Exploiter les données massives requière un nombre considérable de décisions de la part de l'analyste des données pour trouver quelle est la meilleure façon pour stocker, partager et traiter les collections de données afin d'en obtenir le maximum de bénéfice et de connaissances à partir de ces données. Au lieu d'explorer manuellement les collections des données, CURARE fournit de outils intégrés à un environnement pour assister les analystes des données à trouver quelle est la meilleure collection qui peut être utilisée pour accomplir un objectif analytique donné. Nous avons implémenté CURARE et expliqué comment le déployer selon un modèle d'informatique dans les nuages (cloud computing) utilisant des services de science des donnés sur lesquels les services CURARE sont branchés. Nous avons conçu des expériences pour mesurer les coûts de la construction des vues à partir des ensembles des données du Grand Lyon et de Twitter, afin de pourvoir un aperçu de l'intérêt de notre approche et notre environnement de curation de données / The emergence of new platforms for decentralized data creation, such as sensor and mobile platforms and the increasing availability of open data on the Web, is adding to the increase in the number of data sources inside organizations and brings an unprecedented Big Data to be explored. The notion of data curation has emerged to refer to the maintenance of data collections and the preparation and integration of datasets, combining them to perform analytics. Curation tasks include extracting explicit and implicit meta-data; semantic metadata matching and enrichment to add quality to the data. Next generation data management engines should promote techniques with a new philosophy to cope with the deluge of data. They should aid the user in understanding the data collections’ content and provide guidance to explore data. A scientist can stepwise explore into data collections and stop when the content and quality reach a satisfaction point. Our work adopts this philosophy and the main contribution is a data collections’ curation approach and exploration environment named CURARE. CURARE is a service-based system for curating and exploring Big Data. CURARE implements a data collection model that we propose, used for representing their content in terms of structural and statistical meta-data organised under the concept of view. A view is a data structure that provides an aggregated perspective of the content of a data collection and its several associated releases. CURARE provides tools focused on computing and extracting views using data analytics methods and also functions for exploring (querying) meta-data. Exploiting Big Data requires a substantial number of decisions to be performed by data analysts to determine which is the best way to store, share and process data collections to get the maximum benefit and knowledge from them. Instead of manually exploring data collections, CURARE provides tools integrated in an environment for assisting data analysts determining which are the best collections that can be used for achieving an analytics objective. We implemented CURARE and explained how to deploy it on the cloud using data science services on top of which CURARE services are plugged. We have conducted experiments to measure the cost of computing views based on datasets of Grand Lyon and Twitter to provide insight about the interest of our data curation approach and environment

Data life cycle and research data managemet plans are just two of many key-terms used in the present discussion about digital research data. But what do they mean - on the one hand for an individual scholar and on the other hand for a digital infrastructure like IANUS? The presentation will try to explain some of the terms and will show how IANUS is dealing with them in order to enhance the reusability of unique data. The presentation starts with an overview of the different disciplines, research methods and types of data, which together characterise modern research on ancient cultures. Nearly in all scientific processes digital data is produced and has gained a dominant role as the stakeholder-analysis and the evaluation of test data collections done by IANUS in 2013 clearly demonstrate. Nevertheless, inspite of their high relevance digital files and folders are in danger with regard to their accessability and reusability in the near and far future. Not only the storage devices, software applications and file formates become slowly but steadily obsolete, but also the relevant information (i.e. the metadata) to understand all the produced bits and bytes intellectually will get lost over the years. Therefore, urging questions concern the challenges how we can prevent – or at least reduce – a forseeable loss of digital information and what we will do with all the results, which do not find their way into publications? Being a disipline’s specific national center for research data of archaeology and ancient studies, IANUS tries to answer these questions and to establish different services in this context. The slides give an overview of the centre structure, its state of development and its planned targets. The primary service (scheduled for autumn 2016) will be the long-term preservation, curation and publication of digital research data to ensure its reusability and will be open for any person and institution. One already existing offer are the “IT-Empfehlungen für den nachhaltigen Umgang mit digitalen Daten in den Altertumswissenschaften“ which provide information and advice about data management, file formats and project documentation. Furthermore, it offers instructions on how to deposit data collections for archiving and disseminating. Here, external experts are cordially invited to contribute and write missing recommendations as new authors.

Introduction. Previous research has shown that the increasing demands of research data management have proven to be challenging to both researchers and research support. The aim of this thesis is to investigate the process of managing, archiving, and sharing research data and what challenges it faces. Furthermore, it aims to investigate what the archivist’s role is and should be in this process and how archivists and researchers communicate and collaborate with each other.  Method. To pursue this aim, a qualitative method was used, consisting of semi-structured interviews with archivists from seven different public universities in Sweden, with three larger universities and four smaller ones. Analysis. For the analysis a thematic method was used, where similarities and differences, views and opinions were identified and categorized into different themes. Result. The result showed that a fundamental challenge is for the archivists to reach out to the researchers with the information and knowledge that they need and how to make that information comprehensible. The result further showed that several of the interviewed archivists believe that the current focus of resources and demands aimed at the development of open data is misdirected and contra productive because the researchers are more interested in and have a greater need of safe storing and proper archiving of their research data, rather than making it open. Conclusion. The investigation concludes that there is a great need for new ways for archives and other research support to reach out to researchers with information on research data management and that this requires more resources. It furthermore concludes that there is a need to further investigate what researchers truly need for them to properly manage and preserve their research data in the long term and what role the archivist will play in this development.  This is a two years master’s thesis in Archival science.

[pt] Diversas técnicas para extração de informações estruturadas de dados em linguagem natural foram desenvolvidas e demonstraram resultados muito satisfatórios. Entretanto, para obterem tais resultados, requerem uma série de atividades que geralmente são feitas de modo isolado, como a anotação de textos para geração de corpora, etiquetamento morfossintático, engenharia e extração de atributos, treinamento de modelos de aprendizado de máquina etc., o que torna onerosa a extração dessas informações, dado o esforço e tempo a serem investidos. O presente trabalho propõe e desenvolve uma plataforma em ambiente web, chamada LER (Learning Entities and Relations) que integra o fluxo necessário para essas atividades, com uma interface que visa a facilidade de uso. Outrossim, o trabalho mostra os resultados da implementação e uso da plataforma proposta. / [en] Many techniques for the structured information extraction from natural language data have been developed and have demonstrated their potentials yielding satisfactory results. Nevertheless, to obtain such results, they require some activities that are usually done separately, such as text annotation to generate corpora, Part-Of- Speech tagging, features engineering and extraction, machine learning models training etc., making the information extraction task a costly activity due to the effort and time spent on this. The present work proposes and develops a web based platform called LER (Learning Entities and Relations), that integrates the needed workflow for these activities, with an interface that aims the ease of use. The work also shows the platform implementation and its use.

Introduction. Artificial intelligence is growing in society at large and within libraries specifically. There are both positive and negative consequences of this development. In this essay, ethical issues concerning bias, transparency and integrity are examined in a Library and Information Science context. Method and theory. Qualitative survey questionnaires with questions about how the libraries work with AI, the informant’s thoughts on ethical problems with it and how they saw the library’s future with AI were created and sent to employees at research libraries in Sweden. Nine answers to the questionnaires and one article formed the data for analysis. Employing Anthony Gidden’s structuration theory, the essay uses concepts like access point, ontological security and reembedding of trust. Analysis. A qualitative content analysis was carried out on the data. The analysis employed a thematic sectioning of the analyzed text, where the themes were developed through content analysis of the analyzed data in relation to the previous research presented in the essay. Results. Five different themes were sectioned out from the data; bias, integrity, transparency, curation and media- and information literacy. The answers were sectioned into these themes and compared to what the previous research said about the subject. The results are presented in a thematic overview where each section analyses the answers in the specific theme. Conclusion. When using and developing AI, the libraries can use ethical guidelines and curation to be aware of and counteract building bias into the systems. An important part of the libraries’ work for the development of the democratic society is media- and information literacy and teaching about information technology, which AI and the way it is developed is a part of. This is a two years master’s thesis in Library and Information Science.

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In den letzten Jahren haben Innovationen bei Datenquellen und Methoden für die sozialwissenschaftliche Forschung zugenommen. Diese Forschungsarbeit zielt darauf ab, die Auswirkungen dieser Innovationen auf drei Praxisgemeinschaften besser zu verstehen: qualitativ Forschende, Big Social Data Forschende und Datenkurator*innen. Folgenden Forschungsfragen werden behandelt. RQ1: Wie unterscheidet sich die Kuratierung von Big Social Data und qualitativen Daten? RQ2: Welche Auswirkungen haben diese Ähnlichkeiten und Unterschiede auf die Kuratierung von Big Social Data und qualitativen Daten und was können wir aus der Kombination dieser beiden Communities lernen? Ich beantwortete diese Fragen durch eine Literaturrecherche, in der ich Gemeinsamkeiten zwischen qualitativer Datennachnutzung und Big Social Data identifizierte. Dann führte ich semi-strukturierte Interviews mit den drei Praxisgemeinschaften durch. Die Analyse identifizierte sechs Schlüsselthemen für die qualitative Datennachnutzung und Big Social Data: Kontext, Datenqualität und Vertrauenswürdigkeit, Datenvergleichbarkeit, informierte Einwilligung, Datenschutz und Vertraulichkeit sowie geistiges Eigentum und Dateneigentum. Ich habe außerdem fünf weitere Themen identifiziert: Domänenunterschiede, Strategien für eine verantwortungsvolle Praxis, Fragen der Datenpflege, Menschen oder Inhalte als Untersuchungsobjekte sowie unterschiedliche Schwerpunkte und Ansätze. Die Verbindung dieser drei Praxisgemeinschaften kann ein breiteres Verständnis der Schlüsselfragen unterstützen und zu verantwortungsbewussteren Forschungspraktiken führen. Datenkurator*innen verfügen über die Fähigkeiten und Perspektiven, um zwischen den Praxisgemeinschaften zu übersetzen und eine verantwortungsvolle qualitative Nachnutzung von Daten und Big Social Data zu unterstützen. / Recent years have seen the rise of innovations in data sources and methods for social science research. This research aims to better understand the impact of these innovations on three communities of practice: qualitative researchers, big social researchers, and data curators. I address the following research questions. RQ1: How is big social data curation similar to and different from qualitative data curation? RQ1a: How are epistemological, ethical, and legal issues different or similar for qualitative data reuse and big social research? RQ1b: How can data curation practices support and resolve some of these epistemological and ethical issues? RQ2: What are the implications of these similarities and differences for big social data curation and qualitative data curation, and what can we learn from combining these two conversations? I answered these questions through a literature review, in which I identified issues in common between qualitative data reuse and big social research. Then I conducted semi-structured interviews with the three communities of practice. The research identified six key issues for qualitative data reuse and big social research: context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. I also identified five additional themes: domain differences, strategies for responsible practice, data curation issues, human subjects vs. content, and different focuses and approaches. Connecting these three communities of practice can support a broader understanding of the key issues and lead to more responsible research practices. Data curators have the skills and perspectives to translate between communities of practice and provide guidance for responsible qualitative data reuse and big social data.