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Aim: Geographic range shifts are a common species' response to climate change. While occurrence data are commonly used to estimate species' geographical range shifts, ongoing debate suggests that local abundance data may be increasingly important for the estimates, but few studies have investigated differences between the above two types of data. We aimed to explore whether occurrence and abundance data would result in different patterns of geographic range shifts for marine fishes. Location: Northeast US Continental Shelf, North Sea, and East Bering Sea. Methods: We used bottom trawl datasets since 1968 in the three large marine communities to assess whether data types would affect estimated shifts in marine fish species. The range centroids of individual species were first estimated every year and linear regressions were fitted to estimate shift rates in both longitudinal and latitudinal directions. The average range centroids of the last 5 years were used to compare differences between the data types in species' shifts. We then grouped species by traits to overview species compositions. Results: Significant differences in shift trends between regressions based on annual occurrence‐ and abundance‐based range centroids were found in species' longitudinal shifts, particularly in the Northeast US Continental Shelf and North Sea. Approximately 38.5%–45.9% of fish species in the large marine communities had inconsistent shift directions when estimated by different data types. In comparison with the average range centroids of the last 5 years between the two data types, large changes were identified in the magnitudes of the shift distances towards the east and west. Fish species with inconsistent shifts between the two data types were mostly composed of commercial and demersal species. Main Conclusions: The results provide observed differences over decades and suggest caution on the estimation of species' geographic range shifts using occurrence and abundance data and highlight the differences for future assessments of marine species shifts under climate change. [ABSTRACT FROM AUTHOR]

Statistical analysis has a special relationship to measurement. For it, measurement and measurement theory not only provide the data on which analysis is based, but are also a source of important uncertainties in theoretical, methodological and applied research. A detailed presentation of measurement issues is therefore justified, together with the different approaches and the arguments behind them. After a careful discussion of the everyday and scientific meanings of measurement, the paper deals with the different meanings and conceptions of measurement, and then turn to the drastic change in meaning in social statistics in the mid-20th century. Then the theory of measurement scales, their criticism and proposals for expansion will be discussed, and finally some of the specific aspects of Likert-type statements that cannot be placed within the framework of measurement scales will be examined. One of the conclusions of the paper is that the Stevens system of measurement scales cannot be regarded as an adequate description of data types. [ABSTRACT FROM AUTHOR]

Towards the improved preservation and management of high-quality germplasm resources for Alnus nepalensis, a total of 84 samples from 28 superior A. nepalensis trees were used as an original germplasm collection. An optimal strategy for the creation of a core collection of superior A. nepalensis trees was devised by comparing the validity of 18 germplasm subsets. Each of these germplasm subsets was configured using unweighted average clustering and improved minimum distance stepwise sampling methods that integrated six sampling proportions, three data types, and three genetic distance methods. The germplasm subsets for the original germplasm collection were evaluated by quantitative and qualitative trait independence tests according to the data types. The results revealed that the developed germplasm subsets based on phenotype data and Euclidean genetic distances at 55, 45, 35 and 25% sampling proportions; molecular marker data and Nei's genetic distances at 55, 45, 35, 25 and 15% sampling proportions; and combined phenotype and molecular marker data and mixed genetic distances at 55, 45, 35 and 25% sampling proportions could represent the original germplasm collection. The strategy of phenotype values and Euclidean genetic distance were more appropriate than integrating the phenotype and molecular markers data and mixed genetic distance for the creation of a core collection for superior A. nepalensis trees.Considering the effectiveness, practicability, and cost of the development of a core collection for superior A. nepalensis trees, an optimal strategy included an unweighted average technique, improved minimum distance stepwise sampling method, SRAP markers data, Nei's genetic distance, and a 15% sampling proportion. [ABSTRACT FROM AUTHOR]

It has become a trope to speak of the increasing value of health data in our societies. Such rhetoric is highly performative: it creates expectations, channels and justifies investments in data technologies and infrastructures, and portrays deliberations on political and legal issues as obstacles to the flow of data. Yet, important epistemic and political questions remain unexamined, such as how the value of data is created, what data journeys are envisioned by policies and regulation, and for whom data types are (intended to be) valuable. Drawing on two empirical cases, (a) interviews with physicians on the topic of digital selfcare, and (b) expectations of stakeholders on the use of Real-World Data in clinical trials, as well as existing literature, we propose a typology of what health data help us to do. This typology is intended to foster reflection about the different roles and values that data use unfolds. We conclude by discussing how regulation can better accommodate practices of valuation in the health data domain, with a particular focus on identifying regulatory challenges and opportunities for EU-level policy makers, and how Covid-19 has shed light on new aspects of each case. [ABSTRACT FROM AUTHOR]

Management decision-making is increasingly supported by new data types and advanced predictive analytics tools. Prior research suggests that the inclusion of new data types – such as social media data – in forecasting models can improve forecasting. We explore whether managers' operational decisions differ depending on the data type used by a predictive analytics tool and the consistency of the trend with prior developments. Experimental results show that the extent to which managers use predictions from analytics tools is a joint function of the data type utilized and trend consistency. If a trend predicted by an analytics tool reveals a downward break from prior positive developments (i.e., an unexpected negative trend), managers utilize predictions less if they are mainly based on social media data rather than on traditional accounting data. If a trend predicted by an analytics tool continues a prior positive trend, we do not find such a difference. In supplemental analyses, we explore managers' comfort level and related attitude concerning the data types and find that only in the trend-breaking condition mediation effects are observed. Together, our findings have important implications for the management accounting function that needs to embed knowledge about managers' information utilization to facilitate decision-making. [ABSTRACT FROM AUTHOR]

Usage of the gateway in smart Grid (SG) makes the Grid more flexible, Making Sure the best interoperability in heterogeneous network architecture. The purpose of this gateway in SG will be to handle two communicating standards. In this paper, an FPGA layout for gateway has been presented that ensures the safety and manages heterogeneous Smart meters (SMs) in the Neighbourhood area of the smart Grid. The proposed work solves the Integration of two distinct communication standard Kinds of the smart meter (SM), PRIME and 6LoWPAN, which can be wired and wireless type Criteria. The integration of the SMs was performed using a multiplexer (2:1) and De-multiplexer (1:2) architecture in the SM to Data Concentrator Unit (DCU), and the hardware is executed through Xilinx RTL Simulation. The two MUX which is a wired SM type. The next input is just 6 LoWPAN (IPv6 Low power. The gateway architecture was created, including a Security algorithm AES-128,to provide data security for your 2 SMs data. Simulations are conducted MR-OFDM under distinct constellation sizes are compared to 6LoW PAN protocol, and the results promise a good performance in terms of hardware utilization, delay, and power. Comparison of this work provides better performance concerning hardware utilization. The Proposed architecture introduces a delay of the five ns time delay for its Procedure. The system mentioned above description is verified in the target device xc5vlx330-2ff1760 in the Xilinx ISE14.2. Hence the suggested gateway architecture is proved as an acceptable design for heterogeneous SMs to DCU communication in SG.

Sustainable agriculture is currently being challenged under climate change scenarios since extreme environmental processes disrupt and diminish global food production. For example, drought-induced increases in plant diseases and rainfall caused a decrease in food production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow analyzing a large amount of data to understand agricultural production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow the processing and analysis of large amounts of heterogeneous data for which intelligent IT and high-resolution remote sensing techniques are required. However, the selection of ML algorithms depends on the types of data to be used. Therefore, agricultural scientists need to understand the data and the sources from which they are derived. These data can be structured, such as temperature and humidity data, which are usually numerical (e.g., float); semi-structured, such as those from spreadsheets and information repositories, since these data types are not previously defined and are stored in No-SQL databases; and unstructured, such as those from files such as PDF, TIFF, and satellite images, since they have not been processed and therefore are not stored in any database but in repositories (e.g., Hadoop). This study provides insight into the data types used in Agricultural Big Data along with their main challenges and trends. It analyzes 43 papers selected through the protocol proposed by Kitchenham and Charters and validated with the PRISMA criteria. It was found that the primary data sources are Databases, Sensors, Cameras, GPS, and Remote Sensing, which capture data stored in Platforms such as Hadoop, Cloud Computing, and Google Earth Engine. In the future, Data Lakes will allow for data integration across different platforms, as they provide representation models of other data types and the relationships between them, improving the quality of the data to be integrated. [ABSTRACT FROM AUTHOR]

Database systems store data (big data) for various areas dealing with finance (banking, insurance) and are also an essential part of corporate firms. In the field of biology, however, not much attention has been paid to database systems, with the exception of genetics (RNA, DNA) and human protein. Therefore data storage and subsequent implementation is insufficient for this field. The current situation in the field of data use for the assessment of biological relationships and trends is conditioned by constantly changing requirements, while data stored in simple databases used in the field of biology cannot respond operatively to these changes.In the recent period, developments in technology in the field of histology caused an increase in biological information stored in databases with which database technology did not deal. We proposed a new database for histology with designed data types (data format) in database program Microsoft SQL Server Management Studio. In order that the information to support identification of biological trends and regularities isrelevant, the data must be provided in real time and in the required format at the strategic, tactical and operational levels. We set the data type according to the needs of our database, we used numeric (smallint,numbers, float), text string (nvarchar, varchar) and date. To select, insert, modify and delete data, we used Structured Query Language (SQL), which is currently the most widely used language in relational databases. Our results represent a new database for information about histology, focusing on histological structures in systems of animals. The structure and relational relations of the histology database will help in analysis of big data, the objective of which was to find relations between histological structures in species and the diversity of habitats in which species live.In addition to big data, the successful estimation of biological relationships and trends also requires the rapid accuracy of scientists who derive key information from the data. A properly functioning database for metaanalyses, data warehousing, and data mining includes, in addition to technological aspects, planning, design, implementation, management, and implementation. [ABSTRACT FROM AUTHOR]

Data quality (DQ) is a major concern in citizen science (CS) programs and is often raised as an issue among critics of the CS approach. We examined CS programs and reviewed the kinds of data they produce to inform CS communities of strategies of DQ control. From our review of the literature and our experiences with CS, we identified seven primary types of data contributions. Citizens can carry instrument packages, invent or modify algorithms, sort and classify physical objects, sort and classify digital objects, collect physical objects, collect digital objects, and report observations. We found that data types were not constrained by subject domains, a CS program may use multiple types, and DQ requirements and evaluation strategies vary according to the data types. These types are useful for identifying structural similarities among programs across subject domains. We conclude that blanket criticism of the CS data quality is no longer appropriate. In addition to the details of specific programs and variability among individuals, discussions can fruitfully focus on the data types in a program and the specific methods being used for DQ control as dictated or appropriate for the type. Programs can reduce doubts about their DQ by becoming more explicit in communicating their data management practices.

With the rapid advancement of emerging technologies, a huge amount of data is created and processed in daily life. Nowadays, Cloud Computing (CC) technology is one of the frequently adopted technologies to access and store data over the internet. CC mainly provides on-demand and pay-per-use services to users. However, access control and security are two major issues that users face in a cloud environment. During data access from a cloud server, the searching time of a Data Owner (DO) and the data accessing time are high. Therefore, users utilize more cloud services and pay more. High system overhead is another issue of a cloud environment. In this paper, a novel access control model has been proposed to overcome all these concerns. Here, the Cloud Service Provider (CSP) maintains a temporary table based on the data type and popularity value of the DO for fast and efficient data accessing. The cloud service provider can easily search the data owner by using the table and the data accessing time is remarkably reduced. Experimental results and theoretical analysis of the proposed scheme prove its efficiency over the existing schemes. [ABSTRACT FROM AUTHOR]

[Objectives] In recent years,unmanned surface vehicles(USVs) have developed rapidly and will have wide application prospect. Hence it becomes quite urgent to deal with the massive information management problem on long-endurance USVs.[Methods] According to the characteristics of six types of data in USVs' electronic information system,the topology of USV information management system based on time-series database and the data flow model are proposed,and a set of performance test methods for USV information management system are designed.[Results] The test shows that the USV information management system is able to meet the information management needs of USVs' operation. Compared with the traditional relational database,the compression performance of the system is improved by about 10%. The retrieval performance and aggregation analysis capability are excellent,and the system is high universal applicability.[Conclusions] This system is an excellent solution to the problems in information storage and communication transmission on long-endurance USV.

Bio Statistics can define as the application of mathematical tools used in statistics to field of biological science and medicine statistics. Collection, organization, analysis ,interpretation and presentation of data very important part of bio medical research, which is mainly used in research studies .It deal with aspect of this , including the planning of data collection in term of the design of survey and experiments. Now a day, doing various statistical tests has been made easy by sophisticated computer software. But mainly two things are very important to researcher/ investigator is to choose the appropriate statistical test for the computer to perform based on the nature of data derived from one’s own research. The second is to understand if an analysis was performed appropriately during review and interpretation of others’ research. A basic understanding of biostatistics is needed to understand and interpret the medical literature. The basic step of research is defining the research question, review of literature, formulate hypothesis, preparing study design, data collection, analysis and interpretation of it. Bio statistics is mainly concern with descriptive statistics and inferential statistics. The objective of this article is that to understand the type of data, choose the correct method of presentation and summarization of the data according to nature of data.

Some of the early Research Data Alliance working groups reused the notion of digital objects as digital entities described by metadata and referenced by a persistent identifier. In recent times the FAIR principles became a prominent role as framework for the sustainability of scientific data. Both approaches had always machine actionability, the capability of computational systems to use services on data without human intervention, in their focus. The more technical approach of digital objects turned out to provide a complementary view on several aspects of the policy framework of FAIR from a technical perspective. After a deeper analysis and integration of these concepts by a group of European data experts the discussion intensified on so called FAIR digital objects. But they need to be accompanied by services as building blocks for automated processes. We will describe the components of this framework and its potentials here, and also which services inside this framework are required.

Learning methods, such as conventional clustering and classification, have been applied in diagnosing diseases to categorize samples based on their features. Going beyond clustering samples, membership degrees represent to what degree each sample belongs to a cluster. Variation of membership degrees in each cluster provides information about the cluster as a whole and each sample individually which enables us to have insights toward precision medicine. Membership degrees are measured more accurately through removing restrictions from clustering samples. Bounded Fuzzy Possibilistic Method (BFPM) introduces a membership function that keeps the search space flexible to cluster samples with higher accuracy. The method evaluates samples for their movement from one cluster to another. This technique allows us to find critical samples in advance those with the potential ability to belong to other clusters in the near future. BFPM was applied on metabolomics of individuals in a lung cancer case-control study. Metabolomics as proximal molecular signals to the actual disease processes may serve as strong biomarkers of current disease process. The goal is to know whether serum metabolites of a healthy human can be differentiated from those with lung cancer. Using BFPM, some differences were observed, the pathology data were evaluated, and critical samples were recognized. [ABSTRACT FROM AUTHOR]