SowiDataNet ist eine im Aufbau befindliche Forschungsdateninfrastruktur für die Sozial- und Wirtschaftswissenschaften. Zentraler Baustein ist die Entwicklung eines web-basierten Forschungsdatenrepositoriums, welches es Forscherinnen erlaubt, ihre Daten sicher und dauerhaft zu dokumentieren, zu publizieren und mit anderen Forschern zu teilen. Die Entwicklung dieses neuen Services orientiert sich am konkreten Bedarf der Fachcommunity, was sich u.a. in der Durchführung einer detaillierten Anforderungsanalyse widerspiegelt. Ein spezieller Fokus liegt zudem auf der flexiblen Einbindung des Repositoriums in die praktischen Workflows des institutionellen Forschungsdatenmanagements.
Watch the VIDEO.The case for sharing research data has been strongly made in many parts of the world, but noticeably so in Europe. Open access to research data delivers more value for every funding Euro by enabling data reuse and reducing unnecessary duplication of research. Further, open data can help speed the pace of discovery and allows for reproducibility studies. The European Commission has set out a clear vision for open data in their Horizon Europe proposal. Yet in 2018 only about half of research data are shared, according to surveys of researchers, and a much smaller proportion are shared openly or in ways that maximise discoverability and reuse. Whilst policy implementation remains critical to the uptake of data sharing, this must be joined by greater support and education for researchers, and faster, easier routes to sharing data optimally. We also need to make it worth a researcher's time to share their data. Starting with the case for better data practice, this talk showcases the findings of one of the largest author surveys of its kind on current practices, attitudes and perceptions in data-sharing at the point of scholarly publication. The survey, carried out by Springer Nature in 2018, is based on over 7700 responses from academic researchers - at various levels of their career – in Europe, Asia, America, and Australasia. Responses are from across all subject areas. The resulting data provides a valuable insight into how, where, and why data is currently shared and what the main obstacles to sharing it are.The talk identifies the most "critical areas" – as borne out of the survey findings – that need to be tackled with top priority if we are to accelerate the speed and scope of data-sharing. In closing, we therefore ask – how can we better work together across research libraries, institutions, funders, governments, and publishers, to address and action these "critical areas"? Indeed, it is only by working together that we can unlock the huge potential of research data, namely to improve our knowledge, to address the grand societal challenges, and to help solve some of the most pressing problems in science today.
Purpose – The purpose of the research project was to examine the process of developing a data sharing framework between different public sector organisations.
Design/methodology/approach – A two-year case study of a data sharing project between a UK fire and rescue service, local council, NHS primary care trust and a police force was undertaken.
Findings – It is important to carefully determine the requirements for data sharing, to establish data sharing agreements, to have secure arrangements for data sharing, and to ensure compliance with data protection legislation.
Research limitations/implications – Data sharing between public sector organisations can operate effectively if appropriate care is taken when creating data sharing agreements between partner organisations.
Practical implications – Data sharing can assist in reducing duplication of effort between public sector organisations and can reduce costs and enable more co-ordinated provision of public services.
Originality/value – The detailed analysis of a data sharing case study identified the need for a systematic data sharing framework. Such a framework is proposed and illustrated with practical examples of specification, implementation and evaluation.
AbstractHealth crises, climate change, and technological hazards pose serious managerial and equity challenges for local governments. To effectively navigate the uncertainties and complexity, municipalities are increasingly collaborating with one another and sharing data and information to improve decision‐making. While data sharing fosters effectiveness in responding to threats, it also entails risks. One major concern is that local government managers often lack the knowledge and technical skills required for safe and effective data sharing, exposing municipalities to cyberthreats. Drawing on data sharing and cybersecurity scholarship, we investigate whether increased data sharing among local governments makes cities more or less vulnerable to cyberincidents. We test our hypotheses using data from two national surveys of U.S. local government managers conducted in 2016 and 2018. Our findings contribute to the literature on technology and risk in government by informing both public managers and researchers about the potential threats associated with data sharing.
"More and more researchers want to share research data collected from social media to allow for reproducibility and comparability of results. With this paper we want to encourage them to pursue this aim - despite initial obstacles that they may face. Sharing can occur in various, more or less formal ways. We provide background information that allows researchers to make a decision about whether, how and where to share depending on their specific situation (data, platform, targeted user group, research topic etc.). Ethical, legal and methodological considerations are important for making this decision. Based on these three dimensions we develop a framework for social media sharing that can act as a first set of guidelines to help social media researchers make practical decisions for their own projects. In the long run, different stakeholders should join forces to enable better practices for data sharing for social media researchers. This paper is intended as our call to action for the broader research community to advance current practices of data sharing in the future." (author's abstract)
<i>Background:</i> Genomics research data are often widely shared through a variety of mechanisms including publication, meetings and online databases. Re-identification of research participants from sequence data has been shown possible, raising concerns of participants' privacy. <i>Methods:</i> In 2008–09, we convened 10 focus groups in Durham, N.C. to explore attitudes about how genomic research data were shared amongst the research community, communication of these practices to participants and how different policies might influence participants' likelihood to consent to a genetic/genomic study. Focus groups were audio-recorded and transcripts were complemented by a short anonymous survey. Of 100 participants, 73% were female and 76% African-American, with a median age of 40–49 years. <i>Results:</i> Overall, we found that discussants expressed concerns about privacy and confidentially of data shared through online databases. Although discussants recognized the benefits of data-sharing, they believed it was important to inform research participants of a study's data-sharing plans during the informed consent process. Discussants were significantly more likely to participate in a study that planned to deposit data in a restricted access online database compared to an open access database (p < 0.00001). <i>Conclusions:</i> The combination of the potential loss of privacy with concerns about data access and identity of the research sponsor warrants disclosure about a study's data-sharing plans during the informed consent process.
Demonstration. ; National audience ; Les applications partageant des données sensibles peuvent bénéficier des avantages des systèmes P2P (Peer-to-Peer) mais uniquement si la confidentialité est préservée. Dans nos travaux antérieurs, nous avons proposé PriMod, un modèle de confidentialité pour partage de données P2P qui combine le contrôle d'accès basé sur les objectifs, la confiance et le chiffrement. Nous avons également proposé PriServ, un service basé sur PriMod, implémenté sur une table de hachage distribuée (DHT). Cette démonstration montre le prototype PriServ et souligne les bénéfices de notre approche en terme de préservation de la confidentialité de données au travers d'une application médicale de partage de données. Le scénario utilisé expose des aspects critiques comme la gestion de politiques de confidentialité, la publication de données, la recherche de données et la recherche de droits d'accès personnels.
Demonstration. ; National audience ; Les applications partageant des données sensibles peuvent bénéficier des avantages des systèmes P2P (Peer-to-Peer) mais uniquement si la confidentialité est préservée. Dans nos travaux antérieurs, nous avons proposé PriMod, un modèle de confidentialité pour partage de données P2P qui combine le contrôle d'accès basé sur les objectifs, la confiance et le chiffrement. Nous avons également proposé PriServ, un service basé sur PriMod, implémenté sur une table de hachage distribuée (DHT). Cette démonstration montre le prototype PriServ et souligne les bénéfices de notre approche en terme de préservation de la confidentialité de données au travers d'une application médicale de partage de données. Le scénario utilisé expose des aspects critiques comme la gestion de politiques de confidentialité, la publication de données, la recherche de données et la recherche de droits d'accès personnels.
Demonstration. ; National audience ; Les applications partageant des données sensibles peuvent bénéficier des avantages des systèmes P2P (Peer-to-Peer) mais uniquement si la confidentialité est préservée. Dans nos travaux antérieurs, nous avons proposé PriMod, un modèle de confidentialité pour partage de données P2P qui combine le contrôle d'accès basé sur les objectifs, la confiance et le chiffrement. Nous avons également proposé PriServ, un service basé sur PriMod, implémenté sur une table de hachage distribuée (DHT). Cette démonstration montre le prototype PriServ et souligne les bénéfices de notre approche en terme de préservation de la confidentialité de données au travers d'une application médicale de partage de données. Le scénario utilisé expose des aspects critiques comme la gestion de politiques de confidentialité, la publication de données, la recherche de données et la recherche de droits d'accès personnels.
Data and information are fundamental pieces for effective evidence-based policy making and provision of public services. In recent years, some private firms have been collecting large amounts of data, which, were they available to governments, could greatly improve their capacity to take better policy decisions and to increase social welfare. Business-to-Government (B2G) data sharing can result in substantial benefits for society. It can save costs to governments by allowing them to benefit from the use of data collected by businesses without having to collect the same data again. Moreover, it can support the production of new and innovative outputs based on the shared data by different users. Finally, the data available to government may give only an incomplete or even biased picture, while aggregating complementary datasets shared by different parties (including businesses) may result in improved policies with strong social welfare benefits. The examples assembled by the High Level Expert Group on B2G data sharing show that most of the current B2G data transactions remain one-off experimental pilot projects that do not seem to be sustainable over time. Overall, the volume of B2G operations still seems to be relatively small and clearly sub-optimal from a social welfare perspective. The market does not seem to scale compared to the economic potential for welfare gains in society. There are likely to be significant potential economic benefits from additional B2G data sharing operations. These could be enabled by measures that would seek to improve their governance conditions to contribute to increase the overall number of transactions. To design such measures, it is important to understand the nature of the current barriers for B2G data sharing operations. In this paper, we focus on the more important barriers from an economic perspective: (a) monopolistic data markets, (b) high transaction costs and perceived risks in data sharing and (c) a lack of incentives for private firms to contribute to the production of public benefits. The following reflections are mainly conceptual, since there is currently little quantitative empirical evidence on the different aspects of B2G transactions. Monopolistic data markets. Some firms -like big tech companies for instance- may be in a privileged position as the exclusive providers of the type of data that a public body seeks to access. This position enables the firms to charge a high price for the data beyond a reasonable rate of return on costs. While a monopolistic market is still a functioning market, the resulting price may lead to some governments not being able or willing to purchase the data and therefore may cause social welfare losses. Nonetheless, monopolistic pricing may still be justified from an innovation perspective: it strengthens incentives to invest in more and better data collection systems and thereby increases the supply of data in the long run. In some cases, the data seller may be in a position to price-discriminate between commercial buyers and a public body, charging a lower price to the latter since the data would not be used for commercial purposes. High transaction costs and perceived risks. An important barrier for data sharing comes from the ex-ante costs related to finding a suitable data sharing partner, negotiating a contractual arrangement, re-formatting and cleaning the data, among others. Potentially interested public bodies may not be aware of available datasets or may not be in a position to handle them or understand their advantages and disadvantages. There may also be ex-post risks related to uncertainties in the quality and/or usefulness of the data, the technical implementation of the data sharing deal, ensuring compliance with the agreed conditions, the risk of data leaks to unauthorized third-parties and exposure of personal and confidential data. Lack of incentives. Firms may be reluctant to share data with governments because it might have a negative impact on them. This could be due to suspicions that the data delivered might be used to implement market regulations and to enforce competition rules that could negatively affect firms' profits. Moreover, if firms share data with government under preferential conditions, they may have difficulties justifying the foregone profit to shareholders, since the benefits generated by better policies or public services fuelled by the private data will occur to society as a whole and are often difficult to express in monetary terms. Finally, firms might be afraid of entering into a competitive disadvantage if they provide data to public bodies - perhaps under preferential conditions - and their competitors do not. Several mechanisms could be designed to solve the barriers that may be holding back B2G data sharing initiatives. One would be to provide stronger incentives for the data supplier firm to engage in this type of transactions. These incentives can be direct, i.e., monetary, or indirect, i.e., reputational (e.g. as part of corporate social responsibility programmes). Another way would be to ascertain the data transfer by making the transaction mandatory, with a fair cost compensation. An intermediate way would be based on solutions that seek to facilitate voluntary B2G operations without mandating them, for example by reducing the transaction costs and perceived risks for the provider data supplier, e.g. by setting up trusted data intermediary platforms, or appropriate contractual provisions. A possible EU governance framework for B2G data sharing operations could cover these options.
In: Bulletin of the World Health Organization: the international journal of public health = Bulletin de l'Organisation Mondiale de la Santé, Band 95, Heft 4, S. 243-243
Welcome to the third issue of volume 42 of the IASSIST Quarterly (IQ 42:3, 2018).
The IASSIST Quarterly presents in this issue three papers from geographically widespread countries. We call IASSIST 'International', so I am happy to present papers from three continents in this issue with papers from Zimbabwe, Italy and Canada.
The paper 'The State of Preparedness for Digital Curation and Preservation: A Case Study of a Developing Country Academic Library' is by Phillip Ndhlovu, who works as the institutional repository librarian and liaison librarian, and Thomas Matingwina, who is a lecturer at the Department of Library and Information Service at the National University of Science and Technology (NUST) in Bulawayo, Zimbabwe. Modern day libraries have vast amounts of digital content and the authors noted that because these collections require very different management than the traditional paper-based materials, the new materials' longevity is endangered. Their study assessed the state of preparedness of the NUST Library for digital curation and preservation, including the assessment of awareness, competencies, technology infrastructure, digital disaster preparedness, and challenges to digital curation and preservation. They found a lack of policies, lack of expertise by library staff, and lack of funding.
You might conclude that investigating your own organization and reaching the very well known conclusion that 'we need more money!' is not so surprising. However, you have to take note that the Jeff Rothenberg statement from 1995 that 'Digital information lasts forever – or five years, whichever comes first' has not yet sunk in with politicians and administrators, who will immediately associate the term 'digital' with 'saving money'. This study shows them why this is not a valid connotation. It is a study of a single institution, and as the authors note it cannot be generalized even to other academic libraries in Zimbabwe. However, other libraries - also outside Zimbabwe - have here a good guide for making their own assessment of the digital preparedness of their institution.
The second paper was - as was the paper above - presented at the IASSIST conference in 2018 and is also about the transition from media known for thousands of years to new media and digital forms. Peter Peller presented the paper 'From Paper Map to Geospatial Vector Layer: Demystifying the Process'. He is the Director of the Spatial and Numeric Data Services unit at Libraries and Cultural Resources at the University of Calgary in Canada.
The conversion of raster images of maps to vector data is analogous to OCR technologies extracting words from scanned print documents. Thereby the map information becomes more accessible, and usable in geographic information systems (GIS). An illustrative example is that historical geospatial information can be overlaid in Google Earth. The description of the entire process incorporates examples of the various techniques, including different types of editing. Furthermore, descriptions of the software used in selected studies are listed in the appendix. It is mentioned that 'paper texture and ink spread' can be responsible for introducing noise and errors, so remember to keep the old maps. This is because what is considered noise in one context might become the subject for interesting future research. In addition the software for extracting information will most certainly improve.
For once both the author and we at IASSIST Quarterly have been quite fast. The data for the third paper was collected in late 2017 and the results are presented here only a year later. In October 2017 a message appeared on the IASSIST mail list with the start of the sentence 'I would share the data but...' It quickly generated many ways of completing that sentence. Flavio Bonifacio - who works at Metis Ricerche srl in Torino, Italy - quickly launched a questionnaire sent to members of the mail list and to others from similar communities of interested individuals. The questionnaire was an extension of an earlier one concerning scientists' reuse and sharing of data. The paper includes many tabulations and models showing the background as well as the data sharing attitudes found in the survey. A respondent typology is developed based upon the level of propensity for sharing data and the level of experiencing problems in data sharing into a 2-by-2 table consisting of 'irreducible reluctant', 'reducible reluctant', 'problematic follower', and 'premium follower'.
In the Nordic countries we tend to have the impression that certain services are publicly available and for free. This impression is plainly superficial because we Nordic people also know very well that 'there is no such thing as a free lunch'! All services must be paid for in one way or another. If you have many services that carry no direct cost, it is probably because you - and others - paid for them beforehand through taxation. Because of cuts in the public economy one of the things Flavio Bonifacio wanted to investigate was the question 'Is there a market for selling data-sharing services?' The results imply that 'reducible reluctants' can be a target for services that reduce the problems of that group.
Submissions of papers for the IASSIST Quarterly are always very welcome. We welcome input from IASSIST conferences or other conferences and workshops, from local presentations or papers especially written for the IQ. When you are preparing such a presentation, give a thought to turning your one-time presentation into a lasting contribution. Doing that after the event also gives you the opportunity of improving your work after feedback. We encourage you to login or create an author login to https://www.iassistquarterly.com (our Open Journal System application). We permit authors 'deep links' into the IQ as well as deposition of the paper in your local repository. Chairing a conference session with the purpose of aggregating and integrating papers for a special issue IQ is also much appreciated as the information reaches many more people than the limited number of session participants and will be readily available on the IASSIST Quarterly website at https://www.iassistquarterly.com. Authors are very welcome to take a look at the instructions and layout:
https://www.iassistquarterly.com/index.php/iassist/about/submissions
Authors can also contact me directly via e-mail: kbr@sam.sdu.dk. Should you be interested in compiling a special issue for the IQ as guest editor(s) I will also be delighted to hear from you.
Karsten Boye Rasmussen - November 2018
Sharing data publicly can provide numerous benefits to the data owner, data user, as well as the social work research community as a whole. Given the time and resources required to collect data in randomized controlled trials, gleaning the maximum amount of information from this data is highly desirable. Data sets considered to be exhausted by the primary research team often have valuable information that can be used by researchers with different research interests or analytic skill sets. Sharing these data allows other researchers to use these data to answer their research questions without duplicating the data collection efforts. Sharing data can also increase attention to the work of the primary research team, with papers with open data receiving more citations than those without public data. Engaging in open science practices such as data sharing can lead research to be seen as more trustworthy and reliable.
Introduction: Although researchers recognize that sharing disparate data can improve population health, barriers (technical, motivational, economic, political, legal, and ethical) limit progress. In this paper, we aim to enhance the van Panhuis et al. framework of barriers to data sharing; we present a complementary solutions-based data-sharing process in order to encourage both emerging and established researchers, whether or not in academia, to engage in data-sharing partnerships. Brief Description of Major Components: We enhance the van Panhuis et al. framework in three ways. First, we identify the appropriate stakeholder(s) within an organization (e.g., criminal justice agency) with whom to engage in addressing each category of barriers. Second, we provide a representative sample of specific challenges that we have faced in our data-sharing partnerships with criminal justice agencies, local clinical systems, and public health. Third, and most importantly, we suggest solutions we have found successful for each category of barriers. We grouped our solutions into five core areas that cut across the barriers as well as stakeholder groups: Preparation, Clear Communication, Funding/Support, Non-Monetary Benefits, and Regulatory Assurances. Our solutions-based process model is complementary to the enhanced framework. An important feature of the process model is the cyclical, iterative process that undergirds it. Usually, interactions with new data-sharing partner organizations begin with the leadership team and progress to both the data management and legal teams; however, the process is not always linear. Conclusions and Next Steps: Data sharing is a powerful tool in population health research, but significant barriers hinder such partnerships. Nevertheless, by aspiring to community-based participatory research principles, including partnership engagement, development, and maintenance, we have overcome barriers identified in the van Panhuis et al. framework and have achieved success with various data-sharing partnerships. In the future, systematically studying data-sharing partnerships to clarify which elements of a solutions-based approach are essential for successful partnerships may be helpful to academic and non-academic researchers. The organizational climate is certainly a factor worth studying also because it relates both to barriers and to the potential workability of solutions.
