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Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review
Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources.
Broad consent under the GDPR : an optimistic perspective on a bright future
Broad consent-the act of gaining one consent for multiple potential future research projects-sits at the core of much current genomic research practice. Since the 25th May 2018, the General Data Protection Regulation (GDPR) has applied as valid law concerning genomic research in the EU and now occupies a dominant position in the legal landscape. Yet, the position of the GDPR concerning broad consent has recently been cause for concern in the genomic research community. Whilst the text of the GDPR apparently supports the practice, recent jurisprudence contains language which is decidedly less positive. This article takes an in-depth look at the situation concerning broad consent under the GDPR and-despite the understandable concern flowing from recent jurisprudence-offers a positive outlook. This positive outlook is argued from three perspectives, each of which is significant in defining the current, and ongoing, legitimacy and utility of broad consent under the GDPR: The principled, the legal technical, and the practical. © 2020 The Author(s).
Connecting Islands: Bridging zbMATH and DLMF with Scholix, a blueprint for connecting expert knowledge systems
This paper reports on the recently launched zbMATH Links API. We discuss its potential based on the initial link partner, the National Institute of Standards and Technology Digital Library of Mathematical Functions. As the API provides machine readable data in the links, we show how one can use data from both sources for further analysis. To exemplify the simplicity, we also show how one can use zbMATH’s link data in Jupyter notebooks.
Digital research data: from analysis of existing standards to a scientific foundation for a modular metadata schema in nanosafety
Background: Assessing the safety of engineered nanomaterials (ENMs) is an interdisciplinary and complex process producing huge amounts of information and data. To make such data and metadata reusable for researchers, manufacturers, and regulatory authorities, there is an urgent need to record and provide this information in a structured, harmonized, and digitized way. Results: This study aimed to identify appropriate description standards and quality criteria for the special use in nanosafety. There are many existing standards and guidelines designed for collecting data and metadata, ranging from regulatory guidelines to specific databases. Most of them are incomplete or not specifically designed for ENM research. However, by merging the content of several existing standards and guidelines, a basic catalogue of descriptive information and quality criteria was generated. In an iterative process, our interdisciplinary team identified deficits and added missing information into a comprehensive schema. Subsequently, this overview was externally evaluated by a panel of experts during a workshop. This whole process resulted in a minimum information table (MIT), specifying necessary minimum information to be provided along with experimental results on effects of ENMs in the biological context in a flexible and modular manner. The MIT is divided into six modules: general information, material information, biological model information, exposure information, endpoint read out information and analysis and statistics. These modules are further partitioned into module subdivisions serving to include more detailed information. A comparison with existing ontologies, which also aim to electronically collect data and metadata on nanosafety studies, showed that the newly developed MIT exhibits a higher level of detail compared to those existing schemas, making it more usable to prevent gaps in the communication of information. Conclusion: Implementing the requirements of the MIT into e.g., electronic lab notebooks (ELNs) would make the collection of all necessary data and metadata a daily routine and thereby would improve the reproducibility and reusability of experiments. Furthermore, this approach is particularly beneficial regarding the rapidly expanding developments and applications of novel non-animal alternative testing methods.
Information Provision for Informed Consent Procedures in Psychological Research Under the General Data Protection Regulation: A Practical Guide
Psychological research often involves the collection and processing of personal data from human research participants. The European General Data Protection Regulation (GDPR) applies, as a rule, to psychological research conducted on personal data in the European Economic Area (EEA)—and even, in certain cases, to psychological research conducted on personal data outside the EEA. The GDPR elaborates requirements concerning the forms of information that should be communicated to research participants whenever personal data are collected directly from them. There is a general norm that informed consent should be obtained before psychological research involving the collection of personal data directly from research participants is conducted. The information required to be provided under the GDPR is normally communicated in the context of an informed consent procedure. There is reason to believe, however, that the information required by the GDPR may not always be provided. Our aim in this tutorial is thus to provide general practical guidance to psychological researchers allowing them to understand the forms of information that must be provided to research participants under the GDPR in informed consent procedures.
The transition of zbMATH towards an open information platform for mathematics (II): A two-year progress report
[no abstract available]
International transfers of personal data for health research following Schrems II: a problem in need of a solution
On 16 July 2020, the Court of Justice of the European Union issued their decision in the Schrems II case concerning Facebook's transfers of personal data from the EU to the US. The decision may have significant effects on the legitimate transfer of personal data for health research purposes from the EU. This article aims: (i) to outline the consequences of the Schrems II decision for the sharing of personal data for health research between the EU and third countries, particularly in the context of the COVID-19 pandemic; and, (ii) to consider certain options available to address the consequences of the decision and to facilitate international data exchange for health research moving forward.
NFDI4Chem - A Research Data Network for International Chemistry
Research data provide evidence for the validation of scientific hypotheses in most areas of science. Open access to them is the basis for true peer review of scientific results and publications. Hence, research data are at the heart of the scientific method as a whole. The value of openly sharing research data has by now been recognized by scientists, funders and politicians. Today, new research results are increasingly obtained by drawing on existing data. Many organisations such as the Research Data Alliance (RDA), the goFAIR initiative, and not least IUPAC are supporting and promoting the collection and curation of research data. One of the remaining challenges is to find matching data sets, to understand them and to reuse them for your own purpose. As a consequence, we urgently need better research data management.
An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments
Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task.
An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action
Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.