ODISSEI Conference for Social Science in the Netherlands 2023

    1 November 2023

    The ODISSEI Conference for Social Science in the Netherlands seeks to bring together a community of computational social scientists to discuss data, methods, infrastructure, ethics, and theoretical work related to digital and computational approaches in social science research. ODISSEI, the research infrastructure for social science in the Netherlands, connects researchers with the necessary data, expertise, and resources to conduct ground-breaking research and embrace the computational turn in social enquiry.

    Conference registration: The registration is now closed due to the location’s capacity. If you are interested in participating, please reach out via communications@odissei-data.nl to discuss the possibilities.

    Conference date: 2 November 2023
    Location: Media Plaza (Jaarbeurs), Utrecht, the Netherlands
    Contact: communications@odissei-data.nl
    Streaming: due to the huge interest in participation, the plenary room of the Conference (Progress) will be live streamed.

    Find the pdf programme of the conference here.

    Programme

    Find the preliminary programme and abstracts below.

    With coffee and tea.

    Linnet Taylor is a Professor of International Data Governance at the Tilburg Institute for Law, Technology, and Society (TILT), where she leads the ERC-funded Global Data Justice project. Her research focuses on how new sources of digital data are impacting governance, research on human and economic development, and political representation.

    The title of the presentation: ‘The god’s eye view? Remote data, power and (data) justice’.

    Room: Progress

    Chair: Pearl Dykstra

    This talk will explore the societal implications of the ongoing shift toward linked and enriched social data in research. What are the risks and concerns of the new data sources and analytical practices involved, and are current disciplinary and legal rules sufficient safeguards? Principles of data justice will be explored as benchmarks for the beneficent use of population data.

    ODISSEI Flashtalks will provide an introduction to the core ODISSEI facilities and their support to the researchers.

    • Making sensitive data available via SANE
      Lucas van der Meer, ODISSEI, Erasmus University Rotterdam, School of Social and Behavioural Sciences (EUR-ESSB)
    • Showcase of the DANS Data Station for the Social Sciences and Humanities
      Ricarda Braukmann, Data Archiving and Networked Services (DANS); Jetze Touber, Data Archiving and Networked Services (DANS)
    • Searching data using of the ODISSEI Portal
      Angelica Maria Maineri, ODISSEI, Erasmus University Rotterdam, School of Social and Behavioural Sciences (EUR-ESSB); The ODISSEI Portal Team
    • ODISSEI SoDa fellowship and SoDa Team support
      Erik-Jan van Kesteren, Utrecht university
    • Exploring the significance of employment for the chronically ill. Showcase of the value of linking CBS microdata to survey data from the National Panel of the Chronically Ill and Disabled and the Dutch Healthcare Consumer Panel.
      Annette Scherpenzeel, Netherlands Institute for Health Services Research (Nivel); Anne Brabers, Netherlands Institute for Health Services Research (Nivel)

    Making sensitive data available via SANE
    Lucas van der Meer, ODISSEI, Erasmus University Rotterdam, School of Social and Behavioural Sciences (EUR-ESSB)
    Privacy, copyright, and competition barriers limit the sharing of sensitive data for scientific purposes. We propose the Secure Analysis Environment (SANE): a virtual container in which the researcher can analyse sensitive data, and yet leaves the data provider in complete control. By following the Five Safes principles, SANE will enable researchers to conduct research on data that up until now are hardly available to them. SANE comes in two variants. Tinker SANE allows the researcher to see, manipulate and play with the data. In Blind SANE, the researcher submits an algorithm without being able to see the data and the data provider approves the algorithm and output. SANE uses concepts from the CBS Remote Access Environment, ODISSEI Secure Supercomputer and SURF Data Exchange, to build a generic off-the-shelf solution to be used by any sensitive data provider and researcher. SANE can be used by researchers in any discipline, as illustrated by the involvement of consortia in both the social sciences (ODISSEI) as well as humanities (Clariah). Potential sensitive data providers include the Dutch Chamber of Commerce (KvK), ING,, National Library of the Netherlands (KB) and Netherlands Institute for Sound and Vision (NISV).

    Showcase of the DANS Data Station for the Social Sciences and Humanities
    Ricarda Braukmann, Data Archiving and Networked Services (DANS); Jetze Touber, Data Archiving and Networked Services (DANS)
    DANS is the Dutch national centre of expertise and repository for research data. Our domain-specific data archiving and publishing services enable researchers and data stewards to make their data FAIR and share them for reuse where possible. In this presentation, we would like to showcase our new repository: the DANS Data Station Social Sciences and Humanities (SSH). The release of the DANS Data Station SSH is part of the transition from our previous system EASY to a new repository system based on the open source software Dataverse. The Data Station has a number of new features and improvements that make it easier to archive, publish and find data:
    – When depositing data, researchers have the option to restrict access to particular files if they cannot be openly shared. The data station also allows for new versions of a dataset if the research is updated.
    – Detailed information about the datasets can be added in various metadata fields and we support SSH-specific vocabularies such as the European Language Social Science Thesaurus (ELSST) and vocabularies from CESSDA, the Consortium of European Social Science Data Archives.
    – Datasets are assigned with Persistent Identifiers and the metadata is automatically transferred to the ODISSEI Portal and European data portals to make data findable for others.
    – Finding relevant data in the Data Station is supported through full text and advanced search and various filter options. PDFs, images and videos can be previewed directly.
    In this presentation, we will demo these and other features of the Data Station and guide the audience through the process of archiving and finding data for reuse. We will elaborate on the importance of archiving and publishing data in a trustworthy digital repository like our Data Station and how this supports FAIR data and Open Science.

    Searching data using of the ODISSEI Portal
    Angelica Maria Maineri, ODISSEI, Erasmus University Rotterdam, School of Social and Behavioural Sciences (EUR-ESSB); The ODISSEI Portal Team
    Datasets are often scattered across different institutional, national, and international repositories. In the case of CBS microdata, the only public information about available datasets used to be in pdf files, organised thematically, accessible only via the CBS website. This fragmented landscape limits the findability of new data sources a researcher might not be accustomed to, and makes the process of selecting an appropriate data source time-consuming and inefficient. The goal of the ODISSEI Portal is to enable and facilitate the discovery of social sciences datasets across various data providers in the Netherlands within a single interface. The Portal currently collects metadata from social science datasets available at DANS (the Dutch national centre of expertise and repository for research data), DataverseNL and the International Institute for Social History (IISH), from the LISS archive, and from the microdata catalogue available at Statistics Netherlands (CBS). Metadata of these collections is harmonised and enriched using a thesaurus which enables multilingual search, while the user interface allows users to search across all datasets available in these collections to find data relevant for their research. The prototype ODISSEI Portal is publicly available (https://portal.odissei.nl/) in order to be able to get feedback from the community on the design features and functionalities. During the demo presentation, after a brief introduction we will show the latest version of the Portal to the ODISSEI community. A virtual “suggestion box” will be made available to all participants in the session.

    ODISSEI SoDa support and fellowship.
    Erik-Jan van Kesteren, Utrecht university

    The ODISSEI SoDa team helps social scientists with data intensive and computational research. If you work at an ODISSEI organization, we will even do this for free! Visit our booth at the conference so you can:

    • chat with us about your research, see how we can help you.
    • sign up for our upcoming workshops (e.g., supercomputing with the OSSC).
    • look at some projects we have done in the past.
    • try out creating synthetic data with our metasyn software.
    • ask us all about our new fellowship opportunity!

    If you want to know more about what we do and how we do it, visit https://odissei-soda.nl.

    Exploring the significance of employment for the chronically ill. Showcase of the value of linking CBS microdata to survey data from the National Panel of the Chronically Ill and Disabled and the Dutch Healthcare Consumer Panel.
    Annette Scherpenzeel, Netherlands Institute for Health Services Research (Nivel); Anne Brabers, Netherlands Institute for Health Services Research (Nivel)
    The prevalence of chronic conditions is expected to rise in the coming years due to population aging and factors like sedentary lifestyles. Our study focuses on the labor participation and quality of life of persons with chronic conditions compared to the general population. For this aim, we linked survey data from the National Panel of the Chronically Ill and Disabled (NPCD) and the Dutch Healthcare Consumer Panel (CoPa, a sample of the general population) with registration data on employment history from Statistics Netherlands (CBS). Both panels are managed by the Nivel.
    Our findings showed that persons with chronic conditions, who have not yet retired, were less likely to be working over a four-year period than those without such conditions. Moreover, currently employed persons with chronic conditions experienced more unemployment and illness/disability benefit episodes in their recent history compared to those without chronic illnesses. Labor participation was lowest among persons with cardiovascular diseases, followed by diabetes and lung diseases. Employed persons with chronic conditions more often felt a sense of societal inclusion than non-working counterparts, although this varied depending on the specific type of disease.
    In addition to the research findings, we evaluated the process of linking data from the Nivel panels and the CBS, as well as the added value of the linked data. A significant proportion of Nivel panel members consented to the data linkage, and nearly all panel members’ data were successfully linked to the microdata. Our analysis provided insights that could not be obtained from CBS microdata or panel data alone, such as insights into the relationship between chronic conditions, work history, and the sense of belonging in society. This marked the first linkage of Nivel panel data with CBS microdata, a succesful starting point for future projects.

    Throughout the Conference Day, we invite you to attend the ODISSEI Marketplace from 9:00 AM to 5:00 PM. The Marketplace will present a chance to connect with ODISSEI partners and infrastructure providers and gain valuable insights into how you can elevate your research endeavors. Seize this opportunity and join us!

    Room: TransitZone

    Partners and infrastructure providers: 

    • LISS panel – Data Quality, Data Linkage and Innovative Measurement Projects in the LISS panel. Joris Mulder, Centerdata; Marcel Das, Centerdata
    • Portal  – Demo of the ODISSEI Portal. Angelica Maria Maineri, ODISSEI, Erasmus University Rotterdam, School of Social and Behavioural Sciences (EUR-ESSB)
    • SANE  – Are you SANE? Research with sensitive data in the cloud. Martin Brandt, SURF; Annette Langedijk, SURF
    • ODISSEI SoDa  – Social Data Science Team: research support. Erik-Jan van Kesteren, Utrecht University
    • DANS  – DANS: Data Station for the Social Sciences and Humanities. Ricarda Braukmann, Data Archiving and Networked Services (DANS)
    • TDCC – Thematic Digital Competence Centres. Nils Arlinghaus, KNAW
    • ASreview –  An open source machine learning framework for efficient and transparent systematic reviews | Nature Machine Intelligence. Laura Hofstee, Utrecht University

    Data Quality, Data Linkage and Innovative Measurement Projects in the LISS panel.
    Joris Mulder, Centerdata; Marcel Das, Centerdata

    Collecting survey data for your research seems to be a piece of cake nowadays. Free or low-cost online survey tools, platforms like Amazon’s Mechanical Turk or Prolific, or non-probability based self-registration panels are widely available. Although these tools or platforms can be useful for pilot studies or probing whether a certain phenomenon exist in the population, they are not very suitable for making reliable, representative population-level inferences.The LISS panel, which is based on a true probability sample drawn from the population registry by Statistics Netherlands, offers a high-quality online research infrastructure for academic researchers worldwide. It is therefore ideal for research where a good representation of the Dutch population is essential. Founded in 2007 and managed by research institute Centerdata, the panel is composed according to the highest scientific standards. Researchers can field their survey or experiment in the panel or can apply for the yearly LISS ODISSEI call for proposals for funded high-quality data collection. A major advantage is that all data collected in the LISS panel are made available through the LISS Data Archive and can easily be linked to other data, such as the annually fielded longitudinal LISS Core Study, which provides repeated measures for the same individuals and households on a broad range of topics. Furthermore, the data can be linked to registry data from Statistics Netherlands, further enriching your data. The LISS panel also offers data collection through innovative measurement projects. In this presentation we not only discuss data quality and data linkage, but also projects collecting data through wearable devices, speech-to-text technology, data donation of WhatsApp and Google Location data, and the integration of the open source oTree software, which allows for real-time and large-scale online behavioral experiments.

    Demo of the ODISSEI Portal.
    Angelica Maria Maineri, ODISSEI, Erasmus University Rotterdam, School of Social and Behavioural Sciences (EUR-ESSB)

    Datasets are often scattered across different institutional, national, and international repositories. In the case of CBS microdata, the only public information about available datasets used to be in pdf files, organised thematically, accessible only via the CBS website. This fragmented landscape limits the findability of new data sources a researcher might not be accustomed to, and makes the process of selecting an appropriate data source time-consuming and inefficient. The goal of the ODISSEI Portal is to enable and facilitate the discovery of social sciences datasets across various data providers in the Netherlands within a single interface. The Portal currently collects metadata from social science datasets available at DANS (the Dutch national centre of expertise and repository for research data), DataverseNL and the International Institute for Social History (IISH), from the LISS archive, and from the microdata catalogue available at Statistics Netherlands (CBS). Metadata of these collections is harmonised and enriched using a thesaurus which enables multilingual search, while the user interface allows users to search across all datasets available in these collections to find data relevant for their research. The prototype ODISSEI Portal is publicly available (https://portal.odissei.nl/) in order to be able to get feedback from the community on the design features and functionalities. During the demo presentation, after a brief introduction we will show the latest version of the Portal to the ODISSEI community. A virtual “suggestion box” will be made available to all participants in the session.

    Are you SANE? Research with sensitive data in the cloud.
    Martin Brandt, SURF; Annette Langedijk, SURF

    Privacy, copyright, and competition barriers limit the sharing of sensitive data for scientific purposes. We propose the Secure ANalysis Environment (SANE): a virtual environment based on SURF Research Cloud in which the researcher can analyse sensitive data, and yet leaves the data provider in complete control. SANE comes in two variants. Tinker SANE allows the researcher to see, manipulate and play with the data. In Blind SANE, the researcher submits an algorithm without being able to see the data and the data provider approves the algorithm and output. In this live demonstration we showcase the successful prototype of the SANE environment that allowes researchers to analyse copyright-sensitive data of the Dutch National Library, that otherwise would remain unused. We will show both the “Tinker” variant, where the researcher can interact with the data, as well as the “Blind” variant, where only the algorithm has access.

    ODISSEI Social Data Science Team
    Erik-Jan van Kesteren, Utrecht university

    The ODISSEI SoDa team helps social scientists with data intensive and computational research. If you work at an ODISSEI organization, we will even do this for free! Visit our booth at the conference so you can:

    • chat with us about your research, see how we can help you.
    • sign up for our upcoming workshops (e.g., supercomputing with the OSSC).
    • look at some projects we have done in the past.
    • try out creating synthetic data with our metasyn software.
    • ask us all about our new fellowship opportunity!

    If you want to know more about what we do and how we do it, visit https://odissei-soda.nl.

    DANS: Data Station for the Social Sciences and Humanities.
    Ricarda Braukmann, Data Archiving and Networked Services (DANS)

    DANS is the Dutch national centre of expertise and repository for research data. Our domain-specific data archiving and publishing services enable researchers and data stewards to make their data FAIR and share them for reuse where possible. In this presentation, we would like to showcase our new repository: the DANS Data Station Social Sciences and Humanities (SSH). The release of the DANS Data Station SSH is part of the transition from our previous system EASY to a new repository system based on the open source software Dataverse. The Data Station has a number of new features and improvements that make it easier to archive, publish and find data:
    – When depositing data, researchers have the option to restrict access to particular files if they cannot be openly shared. The data station also allows for new versions of a dataset if the research is updated.
    – Detailed information about the datasets can be added in various metadata fields and we support SSH-specific vocabularies such as the European Language Social Science Thesaurus (ELSST) and vocabularies from CESSDA, the Consortium of European Social Science Data Archives.
    – Datasets are assigned with Persistent Identifiers and the metadata is automatically transferred to the ODISSEI Portal and European data portals to make data findable for others.
    – Finding relevant data in the Data Station is supported through full text and advanced search and various filter options. PDFs, images and videos can be previewed directly.
    In this presentation, we will demo these and other features of the Data Station and guide the audience through the process of archiving and finding data for reuse. We will elaborate on the importance of archiving and publishing data in a trustworthy digital repository like our Data Station and how this supports FAIR data and Open Science.

    Thematic Digital Competence Centres (TDCC-NES, TDCC-LSH, TDCC-SSH)
    Nils Arlinghaus, KNAW

    Many research data professionals are already aware of the existence of the Digital Competence Centers (DCC), which offer local support to the data stewards within their institute. More recently, NWO has initiated and funded the launch of three Thematic Digital Competence Centres (TDCC-NES, TDCC-LSH, TDCC-SSH), as part of an assignment from the Ministry of Education, Culture and Science (OCW). These TDCCs are complimentary to the already existing local DCCs and focus on challenges that go beyond individual institutions. The TDCC-SSH will do so in two ways: By building and strengthening a national network, and by offering funding opportunities for collaborative, non-competitive projects that tackle domain-specific challenges. The Thematic DCC Social Sciences & Humanities will be present at the ODISSEI conference to connect and network with others in the research data landscape, to exchange ideas, and to explore potential collaboration opportunities. If you already want to read more about the TDCCs, visit www.TDCC.nl.

    An open source machine learning framework for efficient and transparent systematic reviews | Nature Machine Intelligence
    Laura Hofstee, Utrecht University

    To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.