Data Contributors Research Articles

The Genomic Observatories Metadatabase (GeOMe): A new repository for field and sampling event metadata associated with genetic samples.

The Genomic Observatories Metadatabase (GeOMe, http://www.geome-db.org/) is an open access repository for geographic and ecological metadata associated with biosamples and genetic data. Whereas public databases have served as vital repositories for nucleotide sequences, they do not accession all the metadata required for ecological or evolutionary analyses. GeOMe fills this need, providing a user-friendly, web-based interface for both data contributors and data recipients. The interface allows data contributors to create a customized yet standard-compliant spreadsheet that captures the temporal and geospatial context of each biosample. These metadata are then validated and permanently linked to archived genetic data stored in the National Center for Biotechnology Information’s (NCBI’s) Sequence Read Archive (SRA) via unique persistent identifiers. By linking ecologically and evolutionarily relevant metadata with publicly archived sequence data in a structured manner, GeOMe sets a gold standard for data management in biodiversity science.

Authorized Equi-join for Multiple Data Contributors in the PKC-Based Setting

Authorized Equi-join for Multiple Data Contributors in the PKC-Based Setting

Legal and Ethical Issues around Incorporating Traditional Knowledge in Polar Data Infrastructures

Human knowledge of the polar region is a unique blend of Western scientific knowledge and local and indigenous knowledge. It is increasingly recognized that to exclude Traditional Knowledge from repositories of polar data would both limit the value of such repositories and perpetuate colonial legacies of exclusion and exploitation. However, the inclusion of Traditional Knowledge within repositories that are conceived and designed for Western scientific knowledge raises its own unique challenges. There is increasing acceptance of the need to make these two knowledge systems interoperable but in addition to the technical challenge there are legal and ethical issues involved. These relate to ‘ownership’ or custodianship of the knowledge; obtaining appropriate consent to gather, use and incorporate this knowledge; being sensitive to potentially different norms regarding access to and sharing of some types of knowledge; and appropriate acknowledgement for data contributors. In some cases, respectful incorporation of Traditional Knowledge may challenge standard conceptions regarding the sharing of data, including through open data licensing. These issues have not been fully addressed in the existing literature on legal interoperability which does not adequately deal with Traditional Knowledge. In this paper we identify legal and ethical norms regarding the use of Traditional Knowledge and explore their application in the particular context of polar data. Drawing upon our earlier work on cybercartography and Traditional Knowledge we identify the elements required in the development of a framework for the inclusion of Traditional Knowledge within data infrastructures.

THE IMPACT PROJECT: IMPROVING THE IMPACT OF EXISTING MALARIA PRODUCTS – ACTS

BackgroundThe antimalarial dihydroartemisinin-piperaquine (DHA-PPQ) is one of the recommended drugs to treat uncomplicated Plasmodium falciparum malaria. However, DHA-PPQ has a relatively narrow, poorly defined therapeutic dose range and it is...

Water quality data for national‐scale aquatic research: The Water Quality Portal

AbstractAquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third‐party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.

Abuse Reporting and the Fight Against Cybercrime

Cybercriminal activity has exploded in the past decade, with diverse threats ranging from phishing attacks to botnets and drive-by-downloads afflicting millions of computers worldwide. In response, a volunteer defense has emerged, led by security companies, infrastructure operators, and vigilantes. This reactionary force does not concern itself with making proactive upgrades to the cyber infrastructure. Instead, it operates on the front lines by remediating infections as they appear. We construct a model of the abuse reporting infrastructure in order to explain how voluntary action against cybercrime functions today, in hopes of improving our understanding of what works and how to make remediation more effective in the future. We examine the incentives to participate among data contributors, affected resource owners, and intermediaries. Finally, we present a series of key attributes that differ among voluntary actions to investigate further through experimentation, pointing toward a research agenda that could establish causality between interventions and outcomes.

Aggregating privatized medical data for secure querying applications

Public and private organizations generate large amounts of data which they are happy to allow others to query as long as it is privatized. (One example is that of medical data which can be used for research purposes.) Aggregation of such data on a cloud provides an opportunity for querying over rich data. This paper provides a solution for sharing sensitive data where large numbers of data contributors publish their privatized data sets which are then aggregated by a cloud manager on a cloud so that data can be made available to anyone who wants to query it. Additionally, our solution determines how aggregated data can be efficiently and effectively queried, while retaining privacy not only of the data, but also of the original data owner, the query and the person querying. We introduce a non-standard diagonal data aggregation method and, by experimental testing, demonstrate that our data querying procedure is efficient, maintains acceptable data privacy and acceptable data utility, along with practical computation and storage costs. Our solution also accepts a number of varied queries including join, aggregate, range, nested, ordered by and pattern matching. Finally, we discuss four potential threats posed by our cloud manager against which our scheme is resistant.

Data trajectories: tracking reuse of published data for transitive credit attribution

The ability to measure the use and impact of published data sets is key to the success of the open data/open science paradigm. A direct measure of impact would require tracking data (re)use in the wild, which is difficult to achieve. This is therefore commonly replaced by simpler metrics based on data download and citation counts. In this paper we describe a scenario where it is possible to track the trajectory of a dataset after its publication, and show how this enables the design of accurate models for ascribing credit to data originators. A Data Trajectory (DT) is a graph that encodes knowledge of how, by whom, and in which context data has been re-used, possibly after several generations. We provide a theoretical model of DTs that is grounded in the W3C PROV data model for provenance, and we show how DTs can be used to automatically propagate a fraction of the credit associated with transitively derived datasets, back to original data contributors. We also show this model of transitive credit in action by means of a Data Reuse Simulator. In the longer term, our ultimate hope is that credit models based on direct measures of data reuse will provide further incentives to data publication. We conclude by outlining a research agenda to address the hard questions of creating, collecting, and using DTs systematically across a large number of data reuse instances in the wild.

Measuring Modified Mass Energy Equivalence in Nutritional Epidemiology: A Proposal to Adapt the Biophysical Modelling Approach

The calculation of net dietary energy is in great triumph on the helm of designing an apt dieting for both the therapeutic and normal diet. There are some procedures in this connection in nutritional science which is relatively time consuming, laboratory tests induced and often the misleading data contributors in view of assuring balanced dieting. The dietician is often at bay to approve an exact dieting to sustain health and nutritional soundness adhering to the existing dietary energy measuring methods because the frequently using methods are not informing the net dietary energy level required at all in correct amount for the sample at a population in a community. The aim of the current study is to make a dot over these ongoing panics exploring an easy and accurate way in prescribing a confounding free diet. The study can divulge an open secret in measuring net dietary energy which is mandatory for dieting practices worldwide to resist the possible health horrors in nutritional epidemiology. The study finding is the Modified Mass Energy Equivalence [equation (xi)] can be an outstanding biophysical model in measuring net dietary energy as a dieting tool in health pedagogy of health science.

Crowdsourcing: It Matters Who the Crowd Are. The Impacts of between Group Variations in Recording Land Cover

Volunteered geographical information (VGI) and citizen science have become important sources data for much scientific research. In the domain of land cover, crowdsourcing can provide a high temporal resolution data to support different analyses of landscape processes. However, the scientists may have little control over what gets recorded by the crowd, providing a potential source of error and uncertainty. This study compared analyses of crowdsourced land cover data that were contributed by different groups, based on nationality (labelled Gondor and Non-Gondor) and on domain experience (labelled Expert and Non-Expert). The analyses used a geographically weighted model to generate maps of land cover and compared the maps generated by the different groups. The results highlight the differences between the maps how specific land cover classes were under- and over-estimated. As crowdsourced data and citizen science are increasingly used to replace data collected under the designed experiment, this paper highlights the importance of considering between group variations and their impacts on the results of analyses. Critically, differences in the way that landscape features are conceptualised by different groups of contributors need to be considered when using crowdsourced data in formal scientific analyses. The discussion considers the potential for variation in crowdsourced data, the relativist nature of land cover and suggests a number of areas for future research. The key finding is that the veracity of citizen science data is not the critical issue per se. Rather, it is important to consider the impacts of differences in the semantics, affordances and functions associated with landscape features held by different groups of crowdsourced data contributors.

Petlab: New Zealand’s national rock catalogue and geoanalytical database

ABSTRACTThe Petlab database is New Zealand’s online national rock and analytical database, and provides an accessible, structured and permanent data repository for the local and global geoscience communities. The database contains locations, descriptions and analyses of rock and mineral samples from onland New Zealand, the offshore New Zealand region, Antarctica and worldwide. It is operated by GNS Science and, as a nationally significant database, currently receives core funding from central government. The major data contributors are GNS Science and Auckland, Massey, Waikato, Victoria, Canterbury and Otago universities, all of whom use the database as a digital catalogue for their rock and mineral collections. Petlab sample information and geoanalytical data can be uploaded, queried, viewed and downloaded at http://pet.gns.cri.nz. Petlab is a valuable tool for geoscientists in research and industry.

Open Research Data in Sub-Saharan Africa: A Bibliometric Study Using the Data Citation Index

The purpose of the study was to explore the status of research data sharing among researchers in sub-Saharan Africa (SSA) and internationally. Relevant data was extracted from the Data Citation Index (DCI) using an advanced search strategy, which was limited to the publication years between 2009 and 2014. Data was analysed to obtain the number of data records by country, institution, subject category, year of publication, and document type as well as the number of citations. A Spearman’s correlation analysis was conducted to gauge the relationship between the data records and research articles. Findings indicate that only 20 (out of 50) countries in sub-Saharan Africa produced at least one data record in the DCI, with South Africa leading the pack with 539 (61.39 %) records followed by Kenya, Cameroon and Ghana. SSA contributes a mere 0.03 % of the world’s research data as compared to 1.4 % of the world’s research articles. Research institutions and universities are the major contributors of research data, which largely focuses on Genetics and Heredity (61.3 %), Biochemistry and Molecular Biology (61.3 %), Agriculture (29.2 %) and Forestry (27.3 %). Citation-wise, the research data has attracted fewer average citations than the articles. A correlational analysis of the data reveals that there is a significant correlation between the publication of data and research articles.

Energy-Efficient Software-Defined Data Collection by Participatory Sensing

Internet of Things (IoT) has been widely used in big data applications, such as intelligent transportation, intelligent tourism, and so on. On the one hand, in order to provide better services, a lot of IoT devices are extensively installed in the city for collecting environmental data. Then, the sensors and communication modules integrated in the IoT devices will consume a lot of energy and bandwidth during data collection and transmission. On the other hand, smartphones and cars have become important parts of people’s daily life, and integrate more and more sensors. Thus, the owners of smartphones and cars can be the environmental data contributors for big data applications. However, people’s activities are not programmed and hard to predict, and data contribution may also cause privacy leakage. In this paper, we propose a multi-role-based participatory sensing architecture, which assigns participants to different work roles. Then, through a coordination mechanism among work roles, the application server can control the flow of data between participants or participants and application server without knowing the details of data, which can reduce the privacy leakage. Meanwhile, the data aggregators can help the application server to ensure the quality of the collected data. Then, the application server can select participants or aggregate data without collecting participants’ personal information and worrying about the quality of the collected data. Furthermore, we also propose a QoI-aware, budget-fairness-based participant selection approach for multi-task participatory systems and provide a suboptimal solution to the defined optimization problem. Finally, we have compared our proposed scheme with existing methods via extensive simulations based on the data set of mobility traces of taxi cabs in Rome, Italy. Extensive simulation results well justify the effectiveness and robustness of our approach.

When data contributors meet multiple crowdsourcers: Bilateral competition in mobile crowdsourcing

With the increasingly wide use of sensor-embedded smartphones, we envision that there will be many crowdsourcers to acquire sensing data from a large population of smartphone contributors. They form a bilateral competition market, where crowdsourcers compete for the limited sensing service and smartphone contributors compete for the limited budget from crowdsourcers. Each crowdsourcer has to select an “optimal” budget that can attract enough smartphone contributions. Each smartphone contributor has to decide the crowdsourcers to join, while a congested crowdsourcer may result in a low reward. To achieve the respective goals of crowdsourcers and smartphone contributors, the underlying rational and characteristics in this bilateral competition market needs to be better understood. In this paper, we present a game theoretic study of such a bilateral competition market. To be more practical, we consider the bounded rationality of smartphone contributors. We formulate the dynamic behavior of smartphone contributors as an evolutionary game and present an algorithm for the implementation of evolution process. To model the competition among crowdsourcers, we use a non-cooperative game. We prove the existence of Nash equilibrium and propose an iterative algorithm to achieve the Nash equilibrium.

Building a European geothermal information network using a distributed e-Infrastructure

ABSTRACTGeothermal data are published using different IT services, formats and content representations, and can refer to both regional and global scale information. Geothermal stakeholders search for information with different aims. E-Infrastructures are collaborative platforms that address this diversity of aims and data representations. In this paper, we present a prototype for a European Geothermal Information Platform that uses INSPIRE recommendations and an e-Infrastructure (D4Science) to collect, aggregate and share data sets from different European data contributors, thus enabling stakeholders to retrieve and process a large amount of data. Our system merges segmented and national realities into one common framework. We demonstrate our approach by describing a platform that collects data from Italian, French, Hungarian, Swiss and Icelandic geothermal data providers.

Residual energy-based adaptive data collection approach for periodic sensor networks

Due to its potential applications and the density of the deployed sensors, distributed wireless sensor networks are one of the highly anticipated key contributors of the big data in the future. Consequently, massive data collected by the sensors beside the limited battery power are the main limitations imposed by such networks. In this paper, we consider a periodic sensor networks (PSNs) where sensors transmit their data to the sink on a periodic basis. We propose an efficient adaptive model of data collection dedicated to PSN, in order to increase the network lifetime and to reduce the huge amount of the collected data. The main idea behind this approach is to allow each sensor node to adapt its sampling rate to the physical changing dynamics. In this way, the oversampling can be minimized and the power efficiency of the overall network system can be further improved. The proposed method is based on the dependence of measurements variance while taking into account the residual energy that varies over time. We study three well known statistical tests based on One-Way Anova model. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. Experiments on real sensors data show that our approach can be effectively used to minimize the amount of data retrieved by the network and conserve energy of the sensors, without loss of fidelity/accuracy.

European Vegetation Archive (EVA): an integrated database of European vegetation plots

AbstractThe European Vegetation Archive (EVA) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation‐ plot databases on a single software platform. Data storage in EVA does not affect on‐going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA, contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large‐scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database.

Regulatory Analytics and Data Architecture (RADAR)

Rapid developments in cloud computing and data science have significantly reduced the cost and expanded the scope of possible analytics in the practise of financial regulation. However, the existing Organizational Architecture of regulatory interaction is still rooted in old models of data that sit in siloes and is not actively integrated or shared. In this paper, we developed a framework to consider how the technology forces of: faster computing; networked data portals; and growth in organizational models for parallelism might facilitate a new Regulatory Data Architecture.In the development of this conception, it is natural to adopt the emerging idea of a Social Machine, as a metaphor for the large-scale interaction of humans and machines via web connections. This view of a more connected regulatory system is put forward as a possible solution to the problem of increasing the degree to which regulators might be: informed; engaged; and agile within the goal of pragmatic attention to financial stability, without impeding entrepreneurial vigour. The challenges to progress are enumerated in the context of developing a view on the Politics of Data, and the emerging Political Economy of Data, as these relate to the ever present frictions between the natural constituencies of: data contributors; data collectors and data consumers. Finally, we put forward the idea of a Financial Intelligence Data Office (FIDO) as a clearing house to facilitate linkage of existing data portals. This proposal is to facilitate public-private experimentation for the development of new Regulatory Analytics to create greater system-wide visibility for enhanced financial stability.

Combined analysis of gene regulatory network and SNV information enhances identification of potential gene markers in mouse knockout studies with small number of samples.

RNA-sequencing is widely used to measure gene expression level at the whole genome level. Comparing expression data from control and case studies provides good insight on potential gene markers for phenotypes. However, discovering gene markers that represent phenotypic differences in a small number of samples remains a challenging task, since finding gene markers using standard differential expressed gene methods produces too many candidate genes and the number of candidates varies at different threshold values. In addition, in a small number of samples, the statistical power is too low to discriminate whether gene expressions were altered by genetic differences or not. In this study, to address this challenge, we purpose a four-step filtering method that predicts gene markers from RNA-sequencing data of mouse knockout studies by utilizing a gene regulatory network constructed from omics data in the public domain, biological knowledge from curated pathways, and information of single-nucleotide variants. Our prediction method was not only able to reduce the number of candidate genes than the differentialy expressed gene-only filtered method, but also successfully predicted significant genes that were reported in research findings of the data contributors.

Quality of Contributed Service and Market Equilibrium for Participatory Sensing

User-contributed or crowd-sourced information is becoming increasingly common. In this paper, we consider the specific case of participatory sensing whereby people contribute information captured by sensors, typically those on a smartphone, and share the information with others. We propose a new metric called quality of contributed service (QCS) which characterizes the information quality and timeliness of a specific real-time sensed quantity achieved in a participatory manner. Participatory sensing has the problem that contributions are sporadic and infrequent. To overcome this, we formulate a market-based framework for participatory sensing with plausible models of the market participants comprising data contributors, service consumers and a service provider. We analyze the market equilibrium and obtain a closed form expression for the resulting QCS at market equilibrium. Next, we examine the effects of realistic behaviors of the market participants and the nature of the market equilibrium that emerges through extensive simulations. Our results show that, starting from purely random behavior, the market and its participants can converge to the market equilibrium with good QCS within a short period of time.