Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models

Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other repositories enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. This study examined the extent to which cleaned data from six pipelines using data cleaning tools (e.g., the GBIF web application, different R packages) affect downstream species distribution models. In addition, we assessed how the pipeline data differ from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false-positives, invalid coordinates, and duplicates, leading to data sets that included between 9,484 (GBIF application) and 5,196 records (manual-guided filtering). The expert data consisted of 703 thoroughly handpicked records, comparable to data from field studies. Although differences in the record numbers were relatively large, stacked species distribution models (sSDM) from the pipelines and the expert data were strongly related (mean Pearson’s r across the pipelines: 0.9986, versus the expert data: 0.9173). The ever-stronger correlations resulted from occurrence information that became increasingly condensed in the course of the workflow (from individual occurrences to collectivized occurrences in grid cells to predicted probabilities in the sSDMs). In sum, our results suggest that the R package-based pipelines reliably identified invalid coordinates. In contrast, the GBIF-filtered data still contained both spatial and taxonomic errors. However, major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of expert taxonomic knowledge. We conclude that application-filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high-quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.

Sistem Pakar Dalam Mengidentifikasi Kenaikan Pangkat Pegawai Negeri Sipil Menggunakan Metode Backward Chaining

Promotion can be interpreted as an element in enforcing the career of Civil Servants (PNS). The promotion to the rank of Civil Servants (PNS) is in the form of an award for work achievements that have been achieved and service to the country after fulfilling certain conditions. At this time there are still many Civil Servants (PNS) who do not understand employee governance such as this promotion and there are still many who do not know what are the completeness of promotions and do not know whether a Civil Servant (PNS) can be promoted. or not. This study aims to make Civil Servants (PNS) know whether it is proven or not to be able to be promoted based on certain conditions that must be met for promotion. The data processed in this study were directly directed by experts. The data is sourced from the staffing of the Regional Office of the Ministry of Religion of West Sumatra Province. The promotion data is processed and developed using an expert system built using PHP programming and MySQL database. In the Expert System in identifying promotions for Civil Servants using the Backward Chaining method, the appropriate and suitable results between expert data and tracking results are very good, so that promotions can be identified properly. It is hoped that the application that has been built in this research can be useful for Civil Servants (PNS) in identifying promotions, and to provide information about promotions.

Assessing intrastate shipments from interstate data and expert opinion

Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate.

American Franchising Evolution: Modern Development of Business-Dream

This article is devoted to the analysis of the current development in the franchising industry in the USA, as the main "model" government, where the franchising business has received its most striking development. The structure of the US franchised business by eight major sectors as of 2019-2020 is presented. Based on expert data and statistical information provided by specialized American franchising organizations, a detailed analysis of each sector of the franchised business in the American economy has been carried out. Each sector is assigned its own rating, development trends are given both for the segment as a whole and for the level of employment, productivity and estimated development forecasts.

Development and analysis of fuzzy expert data for technological adjustment of a grain harvester header

The paper considers the problems of developing and presenting fuzzy expert data on external factors and adjustable parameters of the harvester header. The object domain “Technological adjustment of the harvester header” was studied. On the basis of the data, obtained from four experts a linguistic description of the problem statements was given, linguistic variables were introduced, membership functions were developed, consistency characteristic properties were calculated. The base of fuzzy expert knowledge intended for the unit of obtaining and updating knowledge of the decision support intelligent system by an operator in the field conditions was created. In order to estimate quality of the fuzzy expert data and define the degree of its suitability for application in intelligent information system we used the algorithm which provides setting the quality criteria, availability of feedback with experts to update the data, makes it possible to choose the optimal number of terms of the membership functions. The possibility of taking into account the expert data hierarchy in the given algorithm made it possible to introduce experts ranging according to their qualification, for this purpose Fishburn numbers were used as weightihg factors.

A grid mapping scheme for the flora of Tyumen city: a case study for an invasive and a synanthropic plant species

Biodiversity inventory is one of the widespread fields in biological studies around the world. The understanding the correct distribution of each taxon needs obtaining the complete number of species’ records in the study area. In this research, we compared the completeness of reliable expert data and citizen science data obtained through iNaturalist platform for the urban area of the city Tyumen. The comparison was conducted using the grid mapping scheme developed by us for the study area with grid cell size of 1 × 1 km. As target plants, an invasive species Heracleum sosnowskyi and a synanthropic species Urtica cannabina were selected. We found that neither only expert data nor only citizen science (iNaturalist) data can reflect a reliable distribution of these species in the city Tyumen. We believe that only joint coordinated use of both citizen science data and expert data could provide the relevant and reliable data on species’ distribution.

Content Quality of Lesson Scenarios in Moscow E-School

This work is aimed at assessment of content quality of lesson scenarios in Moscow e-school, comparison of expert data with the features of application of these scenarios in order to reveal strong points and deficiencies in content of lesson scenarios. The considered lesson scenarios are characterized by high quality of material component of educational content. The lesson scenarios substantiate forms and types of control of educational results. Methodological support of the lessons, differentiation and individualization of the content, existence of nonstandard situations stimulating personal participation of students, instructions for a student have been manifested least in the lesson scenarios. High expert quality appraisals of lesson scenarios mostly do not coincide with the popularity of lesson scenarios among the users.