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In this paper we assess differential exposure to flooding in two coastal megacities, New York and Mumbai, both of which suffered major flood-related disasters in the past decade. Specifically, we examine whether the most exposed populations are also the most socially vulnerable. First, we developed Social Vulnerability Indices (SoVIs) for each city with census data. We then overlaid the SoVI scores onto flood extent maps for Hurricane Sandy (New York, October 2012) and the Mumbai flash floods (July 2005), as well as for the evacuation zones for New York, to examine patterns of differential exposure. Our results suggest a degree of differential exposure in New York, especially in the highest flood risk areas, and provide fairly clear evidence for differential exposure in Mumbai. However, differences in the input resolution and confidence in the datasets for Mumbai make the results more uncertain. The paper concludes with a discussion of the policy implications and the data needs for urban spatial vulnerability assessments.

Societal expectations about agricultural production are changing. There are increased demands on issues such as food safety, animal welfare and the impact of agriculture on the environment (land, water and air). These changes have been refl ected in the European Union’s (EU) Common Agricultural Policy (CAP), but information on these issues is lacking and this complicates the required evaluation of policies. The EU Framework 7 project FLINT tries to close this gap by analysing the feasibility of collecting data on these new topics. FLINT has established a data infrastructure with up-to-date farm-level indicators for the monitoring and evaluation of the CAP. The project created a pilot network of more than 1,000 farms to collect a set of sustainability indicators at farm level. The pilot represents farm diversity at EU level, including the different administrative environments in the Member States. This paper sets out the context and the main contributions of the project. Societal expectations about agricultural production are changing. There are increased demands on issues such as food safety, animal welfare and the impact of agriculture on the environment (land, water and air). These changes have been refl ected in the European Union’s (EU) Common Agricultural Policy (CAP), but information on these issues is lacking and this complicates the required evaluation of policies. The EU Framework 7 project FLINT tries to close this gap by analysing the feasibility of collecting data on these new topics. FLINT has established a data infrastructure with up-to-date farm-level indicators for the monitoring and evaluation of the CAP. The project created a pilot network of more than 1,000 farms to collect a set of sustainability indicators at farm level. The pilot represents farm diversity at EU level, including the different administrative environments in the Member States. This paper sets out the context and the main contributions of the project.

The United Nations Convention on the Rights of Persons with Disabilities adopted in 2006 holds States responsible to “…collect appropriate information, including statistical and research data, to enable them to formulate and implement policies…” This recognition has led to an increasing number of countries gathering data on disability at the population level; however, there are currently no gold standards for its measurement and different data collection tools have been used throughout the years to fulfil data needs. Understanding how these differences have influenced the measurement of disability globally is crucial to developing reliable and comparable measures. The purpose of this paper is to describe the varying scope and content of data collection instruments on child disability and to provide a historical snapshot of the rates of reported disability among children. A total of 716 data sources were identified, corresponding to 198 countries covering more than 95% of the world's children. The findings reveal a lack of consistent definitions and measures of disability, which contribute to major challenges in producing reliable and comparable statistics.

Objective To identify factors that impede payer engagement in a health information exchange (HIE), along with organizational and policy strategies that might effectively address the impediments.Materials and Methods Qualitative analysis of semi-structured interviews with leaders from 17 varied payer organizations from across the country (e.g., large, national payers; state Blues plans; local Medicaid managed care plans).Results We found a large gap between payers’ vision of what optimal HIE should be and the current approach to HIE in the United States. Notably, payers sought to be active participants in HIE efforts – both providing claims data and accessing clinical data to support payer HIE use cases. Instead, payers were often asked by HIE efforts only to provide financial support without the option to participate in data exchange, or, when given the option, their data needs were secondary to those of providers.Discussion Efforts to engage payers in pursuit of more robust and sustainable HIE need to better align their value proposition with payer HIE use cases. This will require addressing provider concerns about payer access to clinical data. Policymakers should focus on creating the conditions for broader payer engagement by removing common obstacles, such as low provider engagement in HIE.Conclusion Despite variation in the extent to which payers engaged with current HIE efforts, there was agreement on the vision of optimal HIE and the facilitators of greater payer engagement. Specific actions by those leading HIE efforts, complemented by policy efforts nationally, could greatly increase payer engagement and enhance HIE sustainability.

Spatiotemporal data are those objects that change their position and/or extent over time. There are many applications like geographic information systems, intelligent transportation systems, environmental information systems and mobile communications data management systems which deal with spatiotemporal data. Efficient processing of the spatiotemporal data needs sophisticated indexing schemes. Many indexing structures are available in the literature for accessing such data sets. This paper provides a comprehensive survey, classification and brief description of spatiotemporal data indexing structures for efficient accessing of the data by various types of spatiotemporal queries.

Development of the Australian food composition program, focussing on the enablers and barriers to progress, is reviewed following a process of reference harvesting and unstructured interviews with experts. Strong growth in new data and publications during the 1930s and 1970/80s was followed by more stagnant periods, particularly during the 1990s, enabled by data needs for national nutrition surveys, labelling requirements and national policy needs. From the late 1980s there was a move from paper to computerised tables and then to online databases in the 2000s. Australia's food composition tables have evolved in line with international developments in science and changed data publication methods. Maintaining the timeliness of these databases requires significant investment in new analytical data and skilled scientists to drive this process.

Recent water quality decrease has caused difficult in finding clean water source for people and their daily life. Monitoring on water quality had been carried out many times, from up stream to down stream. It’s necessary to do Online Monitoring on ground and underground water quality continuously, so that the effect of water quality decrease could be detected earlier and handle directly. The output of water quality data needs to be processed so that the society and the decision makers could see the information publicly. So, we need a design of structured database of online and real-time water quality data processing. Water quality data management using structured data base system could make water source data retracing easier. Katakunci : database struktur, online monitoring, real time monitoring

Type 2 diabetes mellitus (DM) and heart failure (HF) are closely related, with the onset of one serving as an independent risk factor for the development or progression of the other. The true impact of their relationship is poorly understood. Since various classes of glucose-lowering therapies have been shown to have differing impact on cardiovascular outcomes, cardiovascular effects of such therapies have been increasingly formally evaluated. Areas covered: With the increasing prevalence of concomitant HF and type 2 DM, HF outcomes serve as important endpoints in trials of glucose-lowering therapies. A thorough literature search of recent cardiovascular outcome trials of glucose-lowering therapies was performed. The authors focus on the availability and extent of ascertainment of data related to HF outcomes in these contemporary clinical trial experiences. Expert commentary: Although early cardiovascular outcome trials did not focus on HF events, these outcomes have been increasingly recognized as meaningful end points in cardiovascular outcome trials. The ascertainment of HF end point data needs to become routine and standardized.

The collection of data for therapeutic purposes when using Serious Games in a home environment is essential to help therapists and medical doctors deliver better therapy. It is necessary to use different sensors for collecting such data. This requires using standards as HL7 as much as possible to provide a general purpose approach. On the other hand, the collected data needs an extensive evaluation and interpretation and tools are required to provide the therapist and medical doctor with meaningful information. In this chapter we offer a closer look to standards and propose tools for the analysis of collected data for later use by therapists and medical doctors as a result of the Rehab@Home project.

CASTOR MDA allows collecting huge amounts of measurements from devices. Such data needs to be preprocessed and analyzed to discover and summarize knowledge that can be consumed by human operators. Helper tools are demonstrated on a realistic multilayer network scenario.

Large corpus of scientific research papers have been available for a long time. However, most of those corpus store only the title and the abstract of the paper. For some domains this information may not be enough to achieve high performance in text mining tasks. This problem has been recently reduced by the growing availability of full text scientific research papers. A full text version provides more detailed information but, on the other hand, a large amount of data needs to be processed. A priori, it is difficult to know if the extra work of the full text analysis has a significant impact in the performance of text mining tasks, or if the effect depends on the scientific domain or the specific corpus under analysis.

In this study, we develop a machine learning classifier that determines Twitter users’ home location with 100 m resolution. Our results suggest up to 0.87 overall accuracy in predicting home location for the City of Chicago. We explore the influence of time span of data collection and location-sharing habits of a user. The classifier accuracy changes by data collection time but larger than one-month time spans do not significantly increase prediction accuracy. An individual’s home location can be ascertained with as few as 0.6 to 1.4 tweets/day or 75 to 225 tweets with an accuracy of over 0.8. Our results shed light on how home location information can be predicted with high accuracy and how long data needs to be collected. On the flip side, our results imply potential privacy issues on publicly available social media data.

The modern era of highly sensitive telescopes is enabling the detection of more and more molecular species in various astronomical environments. Many of these are now being carefully examined for the first time. However, to move beyond detection to more detailed analysis such as radiative transfer modelling, certain molecular properties need to be properly measured and calculated. The importance of contributions from vibrationally excited states or collisional (de-)excitations can vary greatly, depending on the specific molecule and the environment being studied. Here, we discuss the present molecular data needs for detailed radiative transfer modelling of observations of molecular rotational transitions, primarily in the (sub-)millimetre and adjacent regimes, and with a focus on the stellar winds of AGB stars.

The risk assessment of complex inorganic materials requires hazard and exposure data. Their variable composition creates difficulties for experimental testing. Therefore, an alternative constituents-based approach is developed for (eco)toxicological assessments. The principle behind this approach assumes that the (eco)toxicological behaviour of the material is predicted by that of its individual constituents. For most, these constituents, experimental data and for some, even risk assessment data are readily available. Data gaps need to be filled by testing or nontesting approaches. A key element to ensure a proper assessment is that the constituent information is relevant, reliable, and adequate, and is easily accessible to assessors. This chapter gives an overview of the data requirements and data-gathering options for risk assessment of complex inorganic materials, including the most relevant data sources and critical aspects to consider during the data collection.

The genetic relationships of 37 local durians from various villages on the island of Halmahera, North Maluku were identified by 142 morphological characters in the vegetative and generative phases. The study was conducted at the Laboratory of Taxonomic Department of Biology at Brawijaya University from May 2016 until October 2016. The results of genetic relationship analysis from the dendrogram showed that the 37 local durians from Halmahera were divided into four main clusters with a similarity coefficient of 0.71. This suggests a close association of genetic variations for local durian variants based on 142 morphological characters. The genetic relationship also indicates that there is regional specificity to the spread of local durian variants in Halmahera. The results of this study can be used as a basis for recommendations on the genetic diversity level of the local durian germplasm from Halmahera to be developed further, as well as for conservation. However, the current data needs to be supported with other character data from research, to accurately obtain higher kinship data.The genetic relationships of 37 local durians from various villages on the island of Halmahera, North Maluku were identified by 142 morphological characters in the vegetative and generative phases. The study was conducted at the Laboratory of Taxonomic Department of Biology at Brawijaya University from May 2016 until October 2016. The results of genetic relationship analysis from the dendrogram showed that the 37 local durians from Halmahera were divided into four main clusters with a similarity coefficient of 0.71. This suggests a close association of genetic variations for local durian variants based on 142 morphological characters. The genetic relationship also indicates that there is regional specificity to the spread of local durian variants in Halmahera. The results of this study can be used as a basis for recommendations on the genetic diversity level of the local durian germplasm from Halmahera to be developed further, as well as for conservation. However, the current data needs to be supported wi...

Integrating physiological mechanism into ecological risk assessment has multiple advantages, as demonstrated by approaches such as the biotic ligand model, adverse outcomes pathways and mechanistic effects models. However, there is significant additional scope for incorporating physiological data and principles into environmental protection paradigms., Ecological risk assessments principally rely on simplified metrics of organismal sensitivity that do not consider mechanism or biological traits. As such, they are unable to adequately extrapolate from standard laboratory tests to real-world settings, and largely fail to account for the diversity of organisms and environmental variables that occur in natural environments. However, an understanding of how stressors influence organism health can compensate for these limitations. Mechanistic knowledge can be used to account for species differences in basal biological function and variability in environmental factors, including spatial and temporal changes in the chemical, physical and biological milieu. Consequently, physiological understanding of biological function, and how this is altered by stressor exposure, can facilitate proactive, predictive risk assessment. In this perspective article, existing frameworks that utilize physiological knowledge (e.g. biotic ligand models, adverse outcomes pathways and mechanistic effect models), are outlined, and specific examples of how mechanistic understanding has been used to predict risk are highlighted. Future research approaches and data needs for extending the incorporation of physiological information into ecological risk assessments are discussed. Although the review focuses on chemical toxicants in aquatic systems, physical and biological stressors and terrestrial environments are also briefly considered.

Missing traffic data are inevitable due to detector failure or communication failure. Currently, most of imputation methods estimated the missing traffic values by using spatial-temporal information as much as possible. However, it ignores an important fact that spatial-temporal information of the traffic missing data is often incomplete and unavailable. Moreover, most of the existing methods are verified by traffic data from freeway, and their applicability to urban road data needs to be further verified. In this paper, a hybrid method for missing traffic data imputation is proposed using FCM optimized by a combination of PSO algorithm and SVR. In this method, FCM is the basic algorithm and the parameters of FCM are optimized. Firstly, the patterns of missing traffic data are analyzed and the representation of missing traffic data is given using matrix-based data structure. Then, traffic data from urban expressway and urban arterial road are used to analyze spatial-temporal correlation of the traffic data for the determination of the proposed method input. Finally, numerical experiment is designed from three perspectives to test the performance of the proposed method. The experimental results demonstrate that the novel method not only has high imputation precision, but also exhibits good robustness.

Indoor positioning systems answer the need for ubiquitous localisation systems. Frequently, indoor positioning relies on machine learning models developed based on the training data composed of WiFi received signal strength (RSS) vectors observed in different indoor locations. However, this requires expensive collection of RSS vectors in precisely measured locations. In this study, we propose a semi-supervised method, which can reduce the volume of expensive labelled training data and exploit the availability of unlabelled signal strength measurements. The method relies, inter alia, on the measures of similarity among nearest neighbours of unlabelled vectors. Tests performed with a number of testbed areas confirm that the method improves the accuracy of random forest models used to estimate indoor location of mobile terminals.

In this research in progress we present the initial stage of a large ethnographic study at a healthcare network in the US. Our goal is to understand how healthcare organizations in the US use algorithms to improve efficiency (cost saving) and effectiveness (quality) of healthcare. Our preliminary findings illustrate that at the national level, algorithms might be detrimental to healthcare quality because they do not consider (and differentiate) contextual issues such as social and cultural (local) settings. At the practice (hospital/physician) level, they help managing the tradeoff between following national “best practices” and accommodating needs of special patients or particular situations, because hospital-based algorithms can be over-ridden by clinicians. We conclude that, while more data needs to be collected, a responsible use of algorithms requires their constant supervision and their application with respect to specific social and cultural settings.

In this work we give an overview of our investigations of prompt gamma-ray emission in nuclear fission. This work was conducted during the last five years in response to a high priority nuclear data request formulated by the OECD/NEA. The aim was to reveal data deficiencies responsible for a severe under-prediction of the prompt gamma heating in nuclear reactor cores. We obtained new prompt fission gamma-ray spectral (PFGS) data for Cf-252(SF) as well as for thermal-neutron induced fission on U-235(nth,f) and Pu-241(nth,f). In addition, first PFGS measurements with a fast-neutron beam were accomplished, too. The impact of the new data and future data needs are discussed.

Cellulose nanomaterials (CNs) derived from wood fibers are renewable materials with wide applicability for use in consumer products as bio-based composite materials and have the potential to replace petroleum-based materials in many existing and novel applications. Because their nanoscale features may impart novel chemical properties and behaviors, it is necessary to address the environmental and safety aspects of CNs to ensure safety in commercial applications, before wide introduction into society. NANO LCRA, a proposed life cycle risk assessment framework, was used for pre-commercial screening of selected applications of CN as a method for systematically identifying and assessing potential risks of CN from occupational, consumer and environmental exposures throughout the product life cycle. The analysis identifies potential exposure scenarios, evaluates toxicity and assesses the adequacy of available data to characterize risk, highlighting data needs and gaps that must be filled to reduce current uncertainty about CN safety. The analysis revealed that occupational inhalation exposure associated with handling CN as a dry powder was the highest priority data gap including the challenge of quantitative measurement for exposure assessment, followed by gaps in knowledge about the toxicity of CN in consumer use products, such as packaging, particularly for food contact. The NANO LCRA findings were then organized into a roadmap for filling key data gaps to allow safety and sustainability assessment that prioritizes data needs according to risk significance to ensure that uncertainty about the CN safety does not interfere with the commercialization of products to market.

Worldwide data production is increasing both in volume and velocity, and with this acceleration, data needs to be processed in streaming settings as opposed to the traditional store and process model. Distributed streaming frameworks are designed to process such data in real time with reasonable time constraints. Apache Heron is a production-ready large-scale distributed stream processing framework. The network is of utmost importance to scale streaming applications to large numbers of nodes with a reasonable latency. High performance computing (HPC) clusters feature interconnects that can perform at higher levels than traditional Ethernet. In this paper the authors present their findings on integrating Apache Heron distributed stream processing system with two high performance interconnects; Infiniband and Intel Omni-Path and show that they can be utilized to improve performance of distributed streaming applications.

Image data has grown rapidly because of advances on photo capturing devices. In traditional, because the image data has not been huge, most past studies focused on the effectiveness improvement. However, accessing the images from a huge amount of image data needs a large cost. Hence, how to perform efficient image retrieval has been a hot topic in the last few decades. To this end, in this paper, we propose efficient big image data retrieval by using clustering index and parallel computation. In the offline stage, the images are grouped into a number of clusters. In the online stage, the relevant images to the query image are retrieved by a level-wise search. Our intent is to conduct a more efficient image retrieval method in comparison with traditional methods but keep the same effectiveness still. In the experiments, four types of retrieval are compared and our proposed parallelized image data retrieval is much faster than the other compared methods under the very close accuracies.

This working paper looks at the Frascati Manual (FM) framework for Research and Development (R&D) statistics and the System of National Accounts (SNA) framework of comprehensive economic accounts - giving an overview of their shared history and analysing similarities and differences in their approaches to measuring R&D following a fundamental change of the treatment of R&D in the 2008 SNA. The working paper also highlights the various actions taken in the 2015 revision of the FM which respond to National Accounts data needs and thereby aims to provide a common platform for collaboration and dialogue between FM and SNA practitioners. Finally, the FM and SNA R&D statistics are presented and their conceptual, data, and coverage underpinnings compared, helping to elucidate issues that need to be communicated to users so that they can better understand and interpret these related but different R&D statistics.

The U.S. Department of Energy (DOE) Office of Science (SC) operates approximately four dozen large-scale science user facilities (SUFs), each of which generates a tremendous amount of scientific data from experiments, observations and computations. To better understand the data needs and challenges, DOE has run many workshops in recent years to identify and articulate data-centric challenges and opportunities at varying resolution, from facility to community scale. Building on those workshop reports, as well as others from elsewhere in the community, this article goes beyond the findings-recommendations typical of workshop reports to consider how one might structure a broad, technology- and data-centric, coordinated research effort that would realize progress towards solutions that address the well documented challenges and opportunities. We focus on identifying practical issues of strategic relevance, along with offering a view about the focal points for a coordinated research and development effort that would target meeting data-centric needs of a broad set of science users and SUFs. These focal points would, by their nature, engage a spectrum of researchers from computer science, computational and experimental sciences, and data science in a coordinated fashion.

This article focuses on a new approach to using quantitative data being trialled at Library Services, Liverpool John Moores University. By employing business intelligence software to interrogate, combine and analyse a variety of data sources, data is transformed to provide insight into customer needs and preferences, which will better inform services. This case study will help librarians looking to extract more meaning from their data by outlining the different stages that this data needs to go through to transform into insight. This framework is especially suited to environments where the volume of data available to practitioners can prove daunting.

Curriculum learning is a learning technique in which a classifier learns from easy samples first and then from increasingly difficult samples. On similar lines, a curriculum based feature selection framework is proposed for identifying most useful features in a dataset. Given a dataset, first, easy and difficult samples are identified. In general, the number of easy samples is assumed larger than difficult samples. Then, feature selection is done in two stages. In the first stage a fast feature selection method which gives feature scores is used. Feature scores are then updated incrementally with the set of difficult samples. The existing feature selection methods are not incremental in nature; entire data needs to be used in feature selection. The use of curriculum learning is expected to decrease the time needed for feature selection with classification accuracy comparable to the existing methods. Curriculum learning also allows incremental refinements in feature selection as new training samples become available. Our experiments on a number of standard datasets demonstrate that feature selection is indeed faster without sacrificing classification accuracy.

This report describes the results of our data needs assessment of the College of Engineering (CoE). With the growing focus on data, the needs of researchers can be unclear if not studied at the source. This led to the creation of this study in which the investigators interviewed faculty from across multiple departments within CoE. This focused study provided information and insight into the daily research practices related to data. The results of our study provided several categories and trends of data related needs and recommendations going forward as to how to better support the needs identified. Unpublished version This is the final report from a CLIR post-doc fellowship project

Organizations across many sectors are under intense pressure to become data-driven. Yet, for mission-driven organizations, the path to becoming and value of being data-driven is not always clear. We present results from an interview-based study of the role of data in the monitoring and evaluation practices of mission-driven organizations. Instead of leading to productive and empowering data-driven decision making, monitoring and evaluation work is characterized by the erosion of autonomy, data drift, and data fragmentation. Together, these consequences of monitoring and evaluation practices play into a cycle of increasing disempowerment for the mission-driven organization. These findings suggest that the design of information systems should work towards empowering organizations in ways that make sense for their unique data needs and those of their constituents.

The improvement in the technology and the desperate need to store huge data has been increasing steadily. Hadoop uses MapReduce framework for processing big datasets, which consists of namenode and datanode. The hadoop framework technology has been extensively used to store, process, and use the enormous data that are being placed into server. Handling of huge small files is becoming laborious since the namenode has to manage the filenames and its corresponding metadata. For the purpose of fault tolerance, the data needs to be replicated on the data nodes. In order to handle the mammoth number of small files and to reduce the out of memory burden on the namenode, several techniques are being approached which involves HDFS, EHDFS, SFS, HAR, NHAR. In this paper we discuss about the different techniques that are developed to handle the storage of small files and propose a new method to solve the storage issue in hadoop. The new method takes the priority of the file as a distinguishing factor which further helps in reducing the memory usage of namenode.

Due to the popularization of personal 3D printing, individuals without a technical background have opportunities to manipulate 3D data. However, creating 3D data needs technical knowledge and it is difficult for beginners. Especially when we make practical tools for personal belongings or furniture with a 3D printer, it is difficult because of being necessary to design such tools to fit in with objects. In this paper, we propose WireMolding, a 3D modeling approach for practical personal 3D printing with which sketches can be created to construct a 3D model from the bi-dimensional shape of a physical wire mold. We developed a prototype system and demonstrated the usefulness of this concept at the example of use cases.

Data is Information that has not been processed or manipulated, which is not enough for some users. So that the info is useful, the data needs to be prepared and integrated first to become Information. Universitas Muhammadiyah Yogyakarta has a tool to monitor data on Alumni, namely Tracer Study. The data currently available is still not integrated and processed correctly. Now there is a solution for processing data and data by making base data. The purpose of creating this database is to facilitate users in checking data, data that will be stored neatly and integrated into the computer. The method in this study uses the Single DDS method. Methodology This research starts from determining the subject of investigation, primary needs, creating baseline data, analyzing results. The backstage of need is carried out with two data analyzes, and interviews with LPKA (Lembaga Pengelola Kemahasiswaan dan Alumni) or Students and Alumni Management Institutions. Baseline data making in this study is single DDS, which has two data stores, namely Stage and DDS. Data analysis is done by creating reports with Microsoft Office Excel according to user needs. The testing phase is carried out with two processes, namely: ETL Testing and Functional Testing.

Decision making and business support is an important process in data mining and this can be achieved by means of pattern classification and extraction. Since the huge volume of data needs starving knowledge to process and organisation faces many issues in solving those issues. Clustering is an effective technology available to analyse and convert the datasets into meaningful patterns. Clustering in data mining uses various attributes to compute large dataset and meet out the real time issues. The proposed model uses Bayesian hierarchical clustering model with Dirichlet model to resolve the issues in large dataset analysis. Experimental results prove that proposed model experience better clustering efficiency than conventional complete link agglomerative clustering by achieving 92% of clustering accuracy.

Routine Malaria surveillance data is useful for assessing incidence and trends over time and in stratification for targeting of malaria. The reporting completeness and potential bias of such data needs assessment. Approximately 52 million people (68%) live in malaria risk areas in Ethiopia, primarily at altitudes below 2,000 meters. Objective: The general objective of this analysis was to assess the trend and magnitude of Malaria surveillance data by time, place and laboratory result for the last five years in Benishangul Gumuz Region. Descriptive cross-sectional study was used to analyze Malaria surveillance data in terms of time, place and laboratory result. Data cleaning and analysis was made by using excel 2013 software. A total of 96757 expected health facilities were in the region from 2013-2017. A total of 86266 health facilities were reported in the last five years with an average completeness of 89.2%. In the last five years (2013-2017) total of 1186514 clinically and confirmed malaria cases were reported regionally with 2290606 total malaria suspected fever examined with ninety inpatient deaths. Among the total clinically and confirmed cases, 1172244 (98.8%) were outpatients, 14248 (1.2%) were inpatients and 1013046 (85.0%) cases were confirmed by laboratory. Out of the total confirmed malaria cases 811984 (80.2%) cases were due to P.falciparum malaria and 201030 (19.8%) cases were due to P.vivax malaria. There were no other species of malaria reported other than these two species. From the total expected health facilities; a total of 86266 health facilities were reported in the last five years with an average completeness of 89.2% which is above the WHO minimum requirement (80.0%). The malaria reports shows that significant improvement in cases of data quality and management from year to year and currently all zones, Woredas and health facilities including private and NGO facilities are included and reported weekly the malaria surveillance from the data report identified in this study. The average five year regional incidence of confirmed malaria (P. Falciparum and P. Vivax) was less than half the incidence of the clinical and confirmed malaria cases.

Electronic health records (EHRs) are proliferating, and financial incentives encourage their use. Applying Fair Information Practice principles to EHRs necessitates balancing patients' rights to control their personal information with providers' data needs to deliver safe, high-quality care. We describe the technical and organizational challenges faced in capturing patients' preferences for patient-controlled EHR access and applying those preferences to an existing EHR.We established an online system for capturing patients' preferences for who could view their EHRs (listing all participating clinic providers individually and categorically-physicians, nurses, other staff) and what data to redact (none, all, or by specific categories of sensitive data or patient age). We then modified existing data-viewing software serving a state-wide health information exchange and a large urban health system and its primary care clinics to allow patients' preferences to guide data displays to providers.Patients could allow or restrict data displays to all clinicians and staff in a demonstration primary care clinic, categories of providers (physicians, nurses, others), or individual providers. They could also restrict access to all EHR data or any or all of five categories of sensitive data (mental and reproductive health, sexually transmitted diseases, HIV/AIDS, and substance abuse) and for specific patient ages. The EHR viewer displayed data via reports, data flowsheets, and coded and free text data displayed by Google-like searches. Unless patients recorded restrictions, by default all requested data were displayed to all providers. Data patients wanted restricted were not displayed, with no indication they were redacted. Technical barriers prevented redacting restricted information in free textnotes. The program allowed providers to hit a "Break the Glass" button to override patients' restrictions, recording the date, time, and next screen viewed. Establishing patient-control over EHR data displays was complex and required ethical, clinical, database, and programming expertise and difficult choices to overcome technical and health system constraints.Assessing patients' preferences for access to their EHRs and applying them in clinical practice requires wide-ranging technical, clinical, and bioethical expertise, to make tough choices to overcome significant technical and organization challenges.

Measurements underpin the engineering decisions that allow products to be designed, manufactured, operated, and maintained. Therefore, the quality of measured data needs to be systematically assured to allow decision makers to proceed with confidence. The use of standards is one way of achieving this. This paper explores the relevance of international documentary standards to the assessment of measurement system capability in High Value Manufacturing (HVM) Industry. An internal measurement standard is presented which supplements these standards and recommendations are made for a cohesive effort to develop the international standards to provide consistency in such industrial applications.

Biological diversity is one of the most important measures for marine conservation in the high seas. However, data and tools to assess and quantify biodiversity in the high seas are still not well developed. This hinders the development of the open-access tools necessary to assess biodiversity across space and time. Here, we examine prototype online mapping and visualization tools we developed within the OBIS-SEAMAP data center, a thematic node of the Ocean Biogeo- graphic Information System (OBIS) specializing in marine mammal, seabird and sea turtle obser- vations, to facilitate spatiotemporal assessments of biodiversity in the high seas. Our aims are to (1) summarize basic data needs for assessing biodiversity issues in the high seas; (2) discuss tech- nical challenges to address such needs and establish a publicly accessible framework for biodiver- sity assessments; and (3) demonstrate how our prototype framework facilitates these assessments using a preliminary case study. We anticipate that this will serve as a model for marine researchers and managers to develop similar frameworks or improve the existing ones for ecological assess- ments. Of special importance is the application of these assessment tools to emerging high seas biodiversity identification processes such as the UN Convention on Biological Diversity, Ecologi- cally or Biologically Significant Marine Areas.

The objective of this study was to understand issues in risk estimation of exposure of mixture of engineered nanoparticles (ENPs) during consumption of crops and to identify future steps to fill current knowledge gaps. Information about co-occurrence of ENPs in wastewater and surface water and their occurrence in edible plants were obtained from published reports and current knowledge gaps were identified. Analysis indicated that there is a data gaps on (1) co-occurrence of ENPs in irrigated wastewater and plants and (2) their mixture toxicity for oral exposure route. To address identified issues, a four-step approach was proposed: (1) Monitor concentrations of different ENPs in surface water as well as in wastewater, (2) Conduct co-occurrence analysis of different ENPs, (3) Conduct uptake studies in plants using mixture of ENPs and determine uptake of different ENPs in plants and identify interaction effects between ENPs during their uptakes by plants, and (4) Develop groups of co-occurring ENPs influencing a given endpoint from oral exposure route and obtain dose-response data during exposures of mixture of ENPs to animals. This study is one of the few efforts highlighting the issue of data needs for estimating health risks during use of ENPscontaminated wastewater for irrigation. Document Type: Research Article DOI: http://dx.doi.org/10.1166/asl.2014.5459 (http://dx.doi.org/10.1166/asl.2014.5459) Publication date: May 1, 2014 Tools

In this paper, on the basis of the data warehouse integration mechanisms, use Object-oriented analysis methods, take the ITIS’s functions and data needs as the starting point, promote and optimize the ETL method and form a Tourism-oriented ITIS data integration model. The “wisdom tourism” is explained elaborately. Then it shows what data integration is and how to use integration.

Introduction: Teeth and dental restorations are resistant to destruction by fire and the elements are therefore useful in identification. This permits accurate identification of a missing child or remains. The Rural Residential free school at Suttur houses a large number of inmates and hence dental records are kept for their identification. Objective: Estimation of Age of children. Methods: Orthopantomographs were used to study for estimation of age of children, using a Nolla’s method of dental age estimation. Results: In this study Nolla’s method underestimated the chronological age of the individuals and underestimation of age increased as the chronological age of the individuals increased. Conclusion: Studies involving larger sample size and population specific data needs to be developed.

The presence of an organization is emphatically impacted by the framework that keeps running inside the organization. A decent framework is a significant prerequisite for an organization. A decent framework is absolutely ready to help all exercises did by the organization in building up its business. One framework that is possessed by the organization as a rule is the bookkeeping framework. This bookkeeping framework is intended to introduce all data identifying with organization exercises. Data that is in all respects firmly identified with bookkeeping frameworks in organizations is money related data that is possessed by the organization. This data is required by different gatherings, both inward gatherings and outside gatherings. This investigation talks about bookkeeping data frameworks for Cellular or Kisel Telecommunications Cooperatives. In light of the aftereffects of research and dialog of the finance bookkeeping data framework at the Cellular Telecommunications Cooperative (Kisel) in Balinusra Region, it tends to be presumed that the bookkeeping data framework in the finance procedure has gone well since it is as per the hypothesis. The utilization of modernized frameworks significantly improves the errand of each part identified with finance in gathering, putting away, and preparing information. PCs associated online are extremely valuable for the data needs required on the grounds that they can get to information and data progressively. Keywords: Accounting Information Systems, Payroll, Software

Construct validation is an important step in the formulation of theory and its testing. Any information infers from the construct and its related hypothesis, without the validation, might be misleading the decision. In the literature, the construct validation guidelines for primary data are available but the exponential usage of secondary data mining approaches in recent times has created its need for secondary data users. However, it is a less addressed domain, specifically for medical sciences due to underdeveloped theoretical foundations of the field. Usually, an assessment of the validity of the secondary data is not performed because primary users already use validation process especially in the tool development phase. Further, secondary data users are more concerned with data mining that is a data-driven approach rather than theoretical knowledge of the field. Therefore, if researchers want to explore the hidden structures of data and try some different combinations of items which were not tested by primary users, then they should validate this newly explored group of items for the creation of knowledge of the particular field. In this paper, a guideline of the construct validation process for secondary data users with its practical issues especially validity and reliability coefficients is discussed. This paper concludes that the construct validation based on secondary data needs special attention of data mining experts and medical researchers while designing the studies to maximize the benefits of the secondary data.

Residents of environmental justice (EJ) communities may bear a disproportionate burden of environmental health risk, and often face additional burdens from social determinants of health. Accounting for cumulative risk should include measures of risk from both environmental sources and social determinants. This study sought to better understand cumulative health risk from both social and environmental sources in a disadvantaged community in Texas. Key outcomes were determining what data are currently available for this assessment, clarifying data needs, identifying data gaps, and considering how those gaps could be filled. Analyses suggested that the traditionally defined EJ community in Port Arthur may have a lower environmental risk from air toxics than the rest of the City of Port Arthur (although the entire city has a higher risk than the average for the state), but may have a larger burden from social determinants of health. However, the results should be interpreted in light of the availability of data, the definitions of community boundaries, and the areal unit utilized. Continued focus on environmental justice communities and the cumulative risks faced by their residents is critical to protecting these residents and, ultimately, moving towards a more equitable distribution and acceptable level of risk throughout society.

We describe an implementation of the Darwin Core Archive (DwC-A) standard that allows for the exchange of biodiversity information contained within the Scratchpads virtual research environment with external collaborators. Using this single archive file Scratchpad users can expose taxonomies, specimen records, species descriptions and a range of other data to a variety of third-party aggregators and tools (currently Encyclopedia of Life, eMonocot Portal, CartoDB, and the Common Data Model) for secondary use. This paper describes our technical approach to dynamically building and validating Darwin Core Archives for the 600+ Scratchpad user communities, which can be used to serve the diverse data needs of all of our content partners.

Extracranial internal carotid artery (ICA) aneurysms are a rare entity. There are very few cases reported in the world literature and most of these have been managed by open surgical techniques, which is the gold standard. Endovascular repair of these aneurysms is now an emerging reality in the modern world. We describe our experience of endovascular repair of a large extracranial internal carotid artery aneurysm which was associated with a highly tortuous and kinked artery and was reaching upto the base of skull. We faced several intraoperative challenges which we would like to share and eventually succeeded. We are happy to report an excellent patient outcome with no morbidity and strongly recommend such techniques for the repair of these aneurysms. But more cases need to be done and more data needs to be acquired as regards long term follow up before we can conclusively state that endovascular option for such aneurysms is the preferred modality.

Purpose: Complete mesocolic excision for right sided colon cancer is a complex operation that might offer a survival benefit. However, studies comparing CME to standard surgery have not been published so far. Methods: One-hundred-seven patients were operated at the discretion of the surgeon either according to principles of complete mesocolic excision (CME-group, n=69) or received a standard right hemicolectomy (non-CME group, n=38). The type of surgery was prospectively recorded. Results: Patients in the non-CME group were significantly older (67,9 vs.78,1 p