Data Curation Process Research Articles

GPU-Enabled Visual Analytics Framework for Big Transportation Datasets

Transportation agencies rely on a variety of data sources for condition monitoring of their assets and making critical decisions such as infrastructure investments and project prioritization. Recent exponential increase in the volumes of these datasets has been causing significant information overload problems for data analysts; data curation process has increasingly become time consuming as legacy CPU-based systems are reaching their limits for processing and visualizing relevant trends in these massive datasets. There is a need for new tools that can consume these new datasets and provide analytics at rates resonant with the speed of human thought. The current paper proposes a new framework that allows for both multidimensional visualization and analytics to be carried seamlessly on large transportation datasets. The framework stores data in a massively parallel database and leverages the immense computational power available in graphical processing units (GPUs) to carry out data analytics and rendering on the fly via a Structured Query Language which interacts with the underlying GPU database. A front-end is designed for near-instant rendering of queried results on simple charts and maps to enable decision makers to drill down insights quickly. The framework is used to develop applications for analyzing big transportation datasets with over 100 million rows. Performance benchmarking experiments conducted showed that the methodology developed is able to provide real-time visual updates for big data in less than 100 ms. The performance of the developed framework was also compared with CPU-based visual analytics platforms such as Tableau and D3.

People of Collections: Facilitators of Interoperability?

In March 2019, the Muséum national d’histoire naturelle, Paris (MNHN) launched the datapoc.mnhn.fr project, funded by the French research infrastructures CollEX-Persée and E-recolnat. This proof of concept was imagined and is supported by a group of partners coming from different communities working at the Muséum (specimen collection curators, librarians, researchers, data scientists, publishers). The initial motivation of this team for getting together was to imagine a way to link the massive data produced and preserved in the heterogeneous institutional collection databases and repositories of the Muséum in order to improve global access and visibility for the benefit of end-users as well as data curation processes. After a year of sharing and deliberating, the group concluded that focusing on people’s names and identification, could be a promising way to explore interoperability and alignment solutions in order to match data hosted in the different systems. The project has thus two main goals: first, to improve biodiversity and taxonomic data quality for the qualification of personal identities, publications and scientific names by resolving frequent ambiguities and issues in people’s names assignment ; second, to develop and assess machine-driven linking strategies between specimen and authorship metadata and resources derived from various institutional datasilos of interest to the research community. In order to test this idea and to experiment innovative data computing and visualization technologies, all parties involved in the project agreed to develop a proof of concept focused on a dataset of 500 names of major MNHN naturalists from its foundation until nowadays. This proof of concept will consist in building a structured authority file for people's names, which could be shared by all services producing and using biodiversity data at MNHN, as well as reusable as open data by external stakeholders and international partners. This structured file will strengthen data and databases production and maintenance workflows, but could also help improving the quality of end-user experience by allowing individuals or machines to match, link or otherwise compute and analyse data that is still difficult to handle because of the diversity of IT applications and limited standardisation practises. It is key to the project that this structured file should somehow comply with international interoperability and semantic web standards so to facilitate global access and data exchanges with similar institutions around the world. Linked datasets and related resources derived from this work will be displayed on a public website designed for researchers as well as for the public via diverse applications and formats (API, RDF). The project will be run from April 2019 to April 2020 by the core team of partners who initiated it, with the support of a private IT and data computing service called Logilab. Some of the challenges of this project include finding an efficient way for building the structured file and then succeed in aligning and disambiguising names already present existing databases. A way to approach this issue is to confront and consolidate MNHN biodiversity datasets with external repositories by using people identifiers systems like ISNI, VIAF, IdREF, which are already familiar to libraries, archives and other cultural institutions. How can those various people identifiers systems be profitable to parse MNHN "people of collections" and help disambiguise them? Is there a particular people identifier system which will prove to be most relevant for all types of collections? Which parsing method will give the best results, and how could it scale up and possibly be reused by other institutions or even future European taxonomic infrastructures? Those are some of the questions the MNHN team is eager to deal with and to share and discuss at the Biodiversity Next Symposium.

The role of CRIS’s in the research life cycle. A case study on implementing a FAIR RDM policy at Radboud University, the Netherlands

In 2015, Radboud University (Nijmegen, the Netherlands) started a project to extend its CRIS (Metis) with functionalities that allow researchers to register (metadata) and archive (uploading files) their research data, while at the same time making the data available for reuse in a FAIR way (via national Dutch data archive DANS). The new functionality was integrated with already existing functions in the CRIS, thus offering a one-stop-shop interface to researchers in which registration and archiving of data is combined with registration of publications, the uploading of full text to the university’s repository, the linking of datasets and publications and the creation of researcher’s profile (CV) pages. Next to the functional extension of the CRIS, the project also included an organizational element: the establishment of support and management structures and workflows, including data curation processes, in order to assure the quality of the data registration process and to foster the FAIRness of the research data.In the period up to now, we continued to transform the university’s CRIS, by bringing it in line with the research life cycle perspective and policy changes in Research Data Management (RDM), including a Data Management Plan (DMP) module and FAIR data.In this paper, it will be argued and demonstrated that both for researchers and research institutes, a CRIS oriented approach to RDM brings added value. We also point to future use cases that put a central role for CRIS’s even earlier within the research life cycle, e.g. at pre-registration of research questions and informed consent/ethics approval procedures. We further envision our CRIS to play a linking pin function between storage and service locations of data during research and at publication. The paper will use Radboud University as a good practice of past, present and future use of CRIS’s in the research life cycle that universities and research institutes as well as researchers and research support desks are currently dealing with in the FAIR data era.

Developing an Environmental Exposure Data Platform for Urban Health Research

An important part of the Canadian Urban Environmental Health Research Consortium’s (CANUE) mandate is developing an operational urban environmental exposure data platform in support of population-based health research. An effective data platform is viewed as key to breaking down some of the existing research silos in the environment and health field and enabling investigation of multiple and interacting environmental influences on population health.In this presentation, we focus on summarizing the types of exposure data in our platform and those we are currently working on, along with how we manage data access and provide user support with value-added content, and custom data processing and visualization tools. In the early stages of developing content for the data platform our efforts focused on integrating data from different national-scale data sources already in existence, many of which were publicly available with published supporting documentation. Continuing efforts focus on developing new data metrics and integrating the many regional and city-specific or, so-called long-tailed data that make up most urban data sets.Our early experience in developing an environmental exposure data platform has provided valuable lessons learned and guided the operation and user support provided by CANUE. In this context, we discuss the data curation process, including data discovery and content selection when presented with multiple datasets. The lessons learned are aimed at data managers that must decide on the value of the data as well as adding value to the data through the curation process. Finally, we discuss some of the emerging challenges in building, sustaining, and future-proofing an environmental exposure data platform for urban health research.

E&P Notes (July 2018)

E&P Notes How ConocoPhillips Solved its Big Data Problem Matt Zborowski, Technology Writer In doing basic analysis on something such as well performance, an engineer can spend weeks merely collecting data related to the subsurface, operations, maintenance, and finance. Staff at Conoco Phillips, however, can bypass this time-consuming data curation process and begin performing analysis right off the bat thanks to a program that was years in the making. Recently scaled across its organization, the Houston independent’s integrated data warehouses (IDWs) serve as centralized data stores for staff involved in various disciplines including operations, production engineering, well construction, reservoir engineering, and geoscience. Deployment has resulted in improved well uptime, decreased drilling times, optimized completion designs, and increased understanding of subsurface characteristics, the company said. Total Prepares Exit From Big Iran Project as US Sanctions Renewed Matt Zborowski, Technology Writer Total said it will pull out of the South Pars 11 (SP11) gas project in Iran to avoid exposure to renewed US sanctions on the country unless the company is granted a waiver. The French supermajor relies heavily on the US financially and has investments and business operations in 23 states spanning upstream, midstream, downstream, and marketing. US banks are involved in more than 90% of the company’s financing operations, US shareholders represent more than 30% of its shareholding, and its US assets represent more than $10 billion of capital employed, it said. US President Donald Trump on 8 May announced that the US would withdraw from the 2015 Joint Comprehensive Plan of Action (JCPOA), commonly referred to as the Iran nuclear deal, and rein-state sanctions enforced before JCPOA took effect, subject to certain winddown periods. European participants in JCPOA remain supportive of the deal. Marriage of Ideas Could Allow More Deepwater Gas to Shore Stephen Rassenfoss, JPT Emerging Technology Senior Editor Lee Thomas admits he and his wife talk about some things most couples don’t. The thing is that he and his spouse, Laura Liebana, are engineers with overlapping interests. His expertise is pipelines and hers is flow assurance. They met while working on pipeline systems used to transport gas from coal seams in Australia. They moved on to offshore work in the Intecsea/Worley Parsons London office where they faced a problem common to those contrasting environments. There are fields in both that produce a mix of gas and liquid and where the energy required to move the mix through a pipe-line significantly limits how much can be produced using natural pressure, among other problems. Shallow Ports Keep US From Exporting Even More Crude Trent Jacobs, Digital Editor The US might be exporting even more oil, and commanding higher prices for it, if only it had the right port facilities. A new report from the US Energy Information Administration (EIA) found that while US crude shipments continue to rise at an impressive rate since a nearly 40-year government ban was lifted in 2015, exports are constrained by an inability to accommodate Very Large Crude Carriers (VLCCs). Exports averaged 1.1 million B/D in 2017 and have expanded to 1.6 million B/D in the first half of this year. Crude exports were less than 500,000 B/D in 2016. Almost all of these exports originate from the US Gulf Coast where most loading facilities are located in shallow inland harbors—a factor that has severely limited the use of VLCCs to move oil overseas. ConocoPhillips Considering Sale of North Sea Assets? Stephen Whitfield, Senior Staff Writer ConocoPhillips is preparing to sell its fields in the North Sea, according to a Reuters report. Citing sources in the oil and gas industry and in banking, the news agency said the operator’s decision is part of an overall effort to focus on US shale operations. While ConocoPhillips has not launched a formal process or appointed a bank to manage a potential sale, the report said that the company’s executives have had discussions with a number of North Sea operators and bankers in the region to test the waters. Reuters said that the sale could net as much as $2 billion, though it was not clear how much of ConocoPhillips’ portfolio would be available, or if the company would put its Norwegian North Sea assets up as well. The company has operated in the North Sea for more than 45 years, with significant developments in both the UK and Norwegian sectors of the North Sea. These developments include fields in Greater Britannia, the J-Area, and the Southern North Sea fields in the UK, along with the Greater Ekofisk Area in Norway.

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).

Introduction:Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data.Methods:PCORnet’s Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals.Results:The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners.Discussion:The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices

Scientific advances build on reproducible researches which need publicly available benchmark data sets. The computer vision and speech recognition communities have led the way in establishing benchmark data sets. There are much less data sets available in mobile computing, especially for rich locomotion and transportation analytics. This paper presents a highly versatile and precisely annotated large-scale data set of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users. The data set comprises seven months of measurements, collected from all sensors of four smartphones carried at typical body locations, including the images of a body-worn camera, while three participants used eight different modes of transportation in the south-east of the U.K., including in London. In total, 28 context labels were annotated, including transportation mode, participant’s posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals. The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 h of labeled data and 17 562 km of traveled distance. We present how we set up the data collection, including the equipment used and the experimental protocol. We discuss the data set, including the data curation process, the analysis of the annotations, and of the sensor data. We discuss the challenges encountered and present the lessons learned and some of the best practices we developed to ensure high quality data collection and annotation. We discuss the potential applications which can be developed using this large-scale data set. In particular, we present how a machine-learning system can use this data set to automatically recognize modes of transportations. Many other research questions related to transportation analytics, activity recognition, radio signal propagation and mobility modeling can be addressed through this data set. The full data set is being made available to the community, and a thorough preview is already published.

Curating and integrating user-generated health data from multiple sources to support healthcare analytics

As the volume and variety of healthcare-related data continue to grow, the analysis and use of this data will increasingly depend on the ability to appropriately collect, curate, and integrate disparate data from many different sources including user-generated health data. We describe our approach to, and highlight our experiences with, the development of a robust data curation process that supports healthcare analytics. The process consists of the following steps: collection, understanding, validation, cleaning, integration, enrichment, and storage. It has been successfully applied to the processing of a variety of data types including clinical data from electronic health records and observational studies, genomic data, microbiome data, self-reported data from surveys, and self-tracked data from wearables from more than 600 subjects. The curated data have been used to support a number of healthcare analytic applications, including descriptive analytics, data visualization, patient stratification, and predictive modeling.

Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations

Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or “pillars”. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as “two sides of the same coin”. Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data product for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g., error propagation, paving the route to improvements in the approach.

ProMusicDB.org: Preserving Legacy With Artist-Centric Professional Music Data Curation

ABSTRACTTypically, curation is performed to preserve legacy of something or someone of significance in art, culture, science, literature, or any other discipline. In most scenarios, it is performed by an expert archivist or historian collecting artifacts and historical references. The more popular and documented the subject, the better the possibility of finding records leading to more comprehensive curation.In the case of music, curation has always been challenging as there are numerous genres, forms and types of music created on a continuous basis across the globe. Music professionals are collaborating and experimenting to produce new music on a regular basis. The activities and performances by each one of them is unique from an art and culture point of view and are, therefore, worthy of preserving.Empowering music professionals to take charge of their own data curation process can be a solution, as when musicians are themselves involved, they can offer first-hand information about every aspect of music they create or are involved in. In addition, musicians can also provide some form of official record to verify their participation. This artist-centric process can help a musician build and organize their complete professional portfolio covering every professional activity with complete metadata about their music career, which they can utilize to market themselves, or track potential income streams.In this article, a case study for ProMusicDB’s Professional Music Data Curation Service will be presented, detailing the process of creating the ProMusicDB profile of veteran artist/professional musician Clydene Jackson.

On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies

Volunteer geographical information (VGI), either in the context of citizen science or the mining of social media, has proven to be useful in various domains including natural hazards, health status, disease epidemics, and biological monitoring. Nonetheless, the variable or unknown data quality due to crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process, in which a quality assurance (QA) is needed, is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF), combining the crowdsourced data into one view, using potentially other data sources as well. Looking at current practices in VGI data quality and using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF. It aims to help in deciding whether a disentanglement can be possible, whether beneficial or not, in understanding the data curation process with respect to its methodology for future usage of crowdsourced data. Analysing situations throughout the data curation process where and when entanglement between QA and DCDF occur, the paper explores the various facets of VGI data capture, as well as data quality assessment and purposes. Far from rejecting the usability ISO quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible while still integrating them within an approach analogous to a Bayesian paradigm.

The First 500 Registrations to the Research Registry®: Advancing Registration of Under-Registered Study Types.

The Declaration of Helsinki 2013 encourages the registration of all research studies involving human participants. However, emphasis has been placed on prospective clinical trials, and it is estimated that only 10% of observational studies are registered. In response, Research Registry® was launched in February 2015; a retrospectively curated registry that is free and easy to use. Research Registry® enables prospective or retrospective registration of studies, including those study types that cannot be registered on existing registries. In this study, we describe the first 500 registrations on Research Registry®. Since the launch of Research Registry® in February 2015, data of registrations have been collected, including type of studies registered, country of origin, and data curation activity. Inappropriate registrations, such as duplicates, were identified by the data curation process. These were removed from the database or modified as required. A quality score was assigned for each registration, based on Sir Austin Bradford Hill's criteria on what research studies should convey. Changes in quality scores over time were assessed. A total of 500 studies were registered on Research Registry® from February 2015 to October 2015, with a total of 1.7 million patients enrolled. The most common study types were retrospective cohort studies (37.2%), case series (14.8%), and first-in-man case reports (10.4%). Registrations were received from 57 different countries; the most submissions were received from Turkey, followed by China and the United Kingdom. Retrospective data curation identified 80 studies that were initially registered as the incorrect study type, and were subsequently correct. The Kruskal-Wallis test identified a significant improvement in quality scores for registrations from February 2015 to October 2015 (p < 0.0001). Since its conception in February 2015, Research Registry® has established itself as a new registry that is free, easy to use, and enables the registration of various study types, including observational studies and first-in-man case reports. Going forward, our plan is to continue developing Research Registry® in line with user feedback and usability studies. We plan to further promote Research Registry® to advance the cause of registration of research, to increase compliance with the Declaration of Helsinki 2013.

Nanocuration workflows: Establishing best practices for identifying, inputting, and sharing data to inform decisions on nanomaterials.

There is a critical opportunity in the field of nanoscience to compare and integrate information across diverse fields of study through informatics (i.e., nanoinformatics). This paper is one in a series of articles on the data curation process in nanoinformatics (nanocuration). Other articles in this series discuss key aspects of nanocuration (temporal metadata, data completeness, database integration), while the focus of this article is on the nanocuration workflow, or the process of identifying, inputting, and reviewing nanomaterial data in a data repository. In particular, the article discusses: 1) the rationale and importance of a defined workflow in nanocuration, 2) the influence of organizational goals or purpose on the workflow, 3) established workflow practices in other fields, 4) current workflow practices in nanocuration, 5) key challenges for workflows in emerging fields like nanomaterials, 6) examples to make these challenges more tangible, and 7) recommendations to address the identified challenges. Throughout the article, there is an emphasis on illustrating key concepts and current practices in the field. Data on current practices in the field are from a group of stakeholders active in nanocuration. In general, the development of workflows for nanocuration is nascent, with few individuals formally trained in data curation or utilizing available nanocuration resources (e.g., ISA-TAB-Nano). Additional emphasis on the potential benefits of cultivating nanomaterial data via nanocuration processes (e.g., capability to analyze data from across research groups) and providing nanocuration resources (e.g., training) will likely prove crucial for the wider application of nanocuration workflows in the scientific community.

ON DATA QUALITY ASSURANCE AND CONFLATION ENTANGLEMENT IN CROWDSOURCING FOR ENVIRONMENTAL STUDIES

Abstract. Volunteer geographical information (VGI) either in the context of citizen science, active crowdsourcing and even passive crowdsourcing has been proven useful in various societal domains such as natural hazards, health status, disease epidemic and biological monitoring. Nonetheless, the variable degrees or unknown quality due to the crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process in which a quality assurance (QA) is needed is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF) combining the crowdsourced data into one view using potentially other data sources as well. Using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF in order to determine whether a disentanglement can be beneficial or not to a better understanding of the data curation process and to its methodology with respect to crowdsourcing data. Far from rejecting the usability quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible but still in integrating them within an approach analogous to a Bayesian paradigm.

SORTA: a system for ontology-based re-coding and technical annotation of biomedical phenotype data.

There is an urgent need to standardize the semantics of biomedical data values, such as phenotypes, to enable comparative and integrative analyses. However, it is unlikely that all studies will use the same data collection protocols. As a result, retrospective standardization is often required, which involves matching of original (unstructured or locally coded) data to widely used coding or ontology systems such as SNOMED CT (clinical terms), ICD-10 (International Classification of Disease) and HPO (Human Phenotype Ontology). This data curation process is usually a time-consuming process performed by a human expert. To help mechanize this process, we have developed SORTA, a computer-aided system for rapidly encoding free text or locally coded values to a formal coding system or ontology. SORTA matches original data values (uploaded in semicolon delimited format) to a target coding system (uploaded in Excel spreadsheet, OWL ontology web language or OBO open biomedical ontologies format). It then semi- automatically shortlists candidate codes for each data value using Lucene and n-gram based matching algorithms, and can also learn from matches chosen by human experts. We evaluated SORTA’s applicability in two use cases. For the LifeLines biobank, we used SORTA to recode 90 000 free text values (including 5211 unique values) about physical exercise to MET (Metabolic Equivalent of Task) codes. For the CINEAS clinical symptom coding system, we used SORTA to map to HPO, enriching HPO when necessary (315 terms matched so far). Out of the shortlists at rank 1, we found a precision/recall of 0.97/0.98 in LifeLines and of 0.58/0.45 in CINEAS. More importantly, users found the tool both a major time saver and a quality improvement because SORTA reduced the chances of human mistakes. Thus, SORTA can dramatically ease data (re)coding tasks and we believe it will prove useful for many more projects.Database URL: http://molgenis.org/sorta or as an open source download from http://www.molgenis.org/wiki/SORTA

Data Curation and the University Archives

Data curation—the active and ongoing management of data through its life cycle—is a concern for researchers. As caretakers, it would seem natural that the university archivist and the university archives have a role in the data curation process. This article addresses the authors' research project to determine the level of involvement of archives and archivists in data curation. The results are contextualized within archival theory and practice, and the authors suggest additional steps that should be taken if the profession is to have a role in data curation.

Southern African Treatment Resistance Network (SATuRN) RegaDB HIV drug resistance and clinical management database: supporting patient management, surveillance and research in southern Africa

Substantial amounts of data have been generated from patient management and academic exercises designed to better understand the human immunodeficiency virus (HIV) epidemic and design interventions to control it. A number of specialized databases have been designed to manage huge data sets from HIV cohort, vaccine, host genomic and drug resistance studies. Besides databases from cohort studies, most of the online databases contain limited curated data and are thus sequence repositories. HIV drug resistance has been shown to have a great potential to derail the progress made thus far through antiretroviral therapy. Thus, a lot of resources have been invested in generating drug resistance data for patient management and surveillance purposes. Unfortunately, most of the data currently available relate to subtype B even though >60% of the epidemic is caused by HIV-1 subtype C. A consortium of clinicians, scientists, public health experts and policy markers working in southern Africa came together and formed a network, the Southern African Treatment and Resistance Network (SATuRN), with the aim of increasing curated HIV-1 subtype C and tuberculosis drug resistance data. This article describes the HIV-1 data curation process using the SATuRN Rega database. The data curation is a manual and time-consuming process done by clinical, laboratory and data curation specialists. Access to the highly curated data sets is through applications that are reviewed by the SATuRN executive committee. Examples of research outputs from the analysis of the curated data include trends in the level of transmitted drug resistance in South Africa, analysis of the levels of acquired resistance among patients failing therapy and factors associated with the absence of genotypic evidence of drug resistance among patients failing therapy. All these studies have been important for informing first- and second-line therapy. This database is a free password-protected open source database available on www.bioafrica.net. Database URL: http://www.bioafrica.net/regadb/

Integration of data: the Nanomaterial Registry project and data curation

Due to the use of nanomaterials in multiple fields of applied science and technology, there is a need for accelerated understanding of any potential implications of using these unique and promising materials. There is a multitude of research data that, if integrated, can be leveraged to drive toward a better understanding. Integration can be achieved by applying nanoinformatics concepts. The Nanomaterial Registry is using applied minimal information about nanomaterials to support a robust data curation process in order to promote integration across a diverse data set. This paper describes the evolution of the curation methodology used in the Nanomaterial Registry project as well as the current procedure that is used. Some of the lessons learned about curation of nanomaterial data are also discussed.

RefPrimeCouch—a reference gene primer CouchApp

To support a quantitative real-time polymerase chain reaction standardization project, a new reference gene database application was required. The new database application was built with the explicit goal of simplifying not only the development process but also making the user interface more responsive and intuitive. To this end, CouchDB was used as the backend with a lightweight dynamic user interface implemented client-side as a one-page web application. Data entry and curation processes were streamlined using an OpenRefine-based workflow. The new RefPrimeCouch database application provides its data online under an Open Database License.Database URL: http://hpclife.th-wildau.de:5984/rpc/_design/rpc/view.html

Data Curation in the OpenAIRE Scholarly Communication Infrastructure

OpenAIRE is the European Union initiative for an Open Access Infrastructure for Research in support of open scholarly communication and access to the research output of European funded projects and open access content from a network of institutional and disciplinary repositories. This article outlines the curation activities conducted in the OpenAIRE infrastructure, which employs a multi-level, multi-targeted approach: the publication and implementation of interoperability guidelines to assist in the local data curation processes, the data curation due to the integration of heterogeneous sources supporting different types of data, the inference of links to accomplish the publication research contextualization and data enrichment, and the end-user metadata curation that allows users to edit the attributes and provide links among the entities.