Data for species distribution models: the environmental data

Online prediction of network-level public transport demand based on principle component analysis

BackgroundData quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues.ObjectivesTo explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task.MethodsWe started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods’ results and corresponding outcome data (data that indicated the data’s suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without.ResultsOur self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods’ results.ConclusionsOur data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods’ results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.

Biophysical conditions, including climate, environmental stress, and habitat availability, are key drivers of many ecological processes (e.g., community assembly and productivity) and associated ecosystem services (e.g., carbon sequestration and fishery production). Furthermore, anthropogenic impacts such as coastal development and fishing can have drastic effects on the structure and function of marine ecosystems. Scientists need to account for environmental variation and human impacts to accurately model, manage, and conserve marine ecosystems. Although there are many types of environmental data available from global remote sensing and open-source data products, some are inaccessible to potential end-users because they exist as global layers in high temporal and spatial resolutions which require considerable computational power to process. Additionally, coastal locations often suffer from missing data or data quality issues which limit the utility of some global marine products for coastal sites. Herein we present the Marine Socio-Environmental Covariates dataset for the global oceans, which consists of environmental and anthropogenic variables summarized in ecologically relevant ways. The dataset includes four sets of environmental variables related to biophysical conditions (net primary productivity models corrected for shallow-water reflectance, wave energy including sheltered-coastline corrections) and landscape context (coral reef and land cover within varying radii). We also present two sets of anthropogenic variables, human population density (within varying radii) and distance to large population center, which can serve as indicators of local human impacts. We have paired global, summarized layers available for download with an online data querying platform that allows users to extract data for specific point locations with finer control of summary statistics. In creating these global layers and online platform, we hope to make the data accessible to a wide array of end-users with the goal of advancing marine ecosystem studies.

Smart manufacturing applications increasingly rely on time-series data from Industrial IoT sensors, yet these data streams often contain data quality issues (DQIs) that affect analysis and disrupt production. While traditional Machine Learning methods are difficult to apply due to the small amount of data available, the knowledge-based approach of Case-Based Reasoning (CBR) offers a way to reuse previously gained experience. We introduce the first end-to-end Case-Based Reasoning (CBR) framework that both detects and remedies DQIs in near real time, even when only a handful of annotated fault instances are available. Our solution encodes expert experience in the four CBR knowledge containers: (i) a vocabulary that represents sensor streams and their context in the DataStream format; (ii) a case base populated with fault-annotated event logs; (iii) tailored similarity measures—including a weighted Dynamic Time Warping variant and structure-aware list mapping—that isolate the signatures of missing-value, missing-sensor, and time-shift errors; and (iv) lightweight adaptation rules that recommend concrete repair actions or, where appropriate, invoke automated imputation and alignment routines. A case study is used to examine and present the suitability of the approach for a specific application domain. Although the case study demonstrates only limited capabilities in identifying Data Quality Issues (DQIs), we aim to support transparent evaluation and future research by publishing (1) a prototype of the Case-Based Reasoning (CBR) system and (2) a publicly accessible, meticulously annotated sensor-log benchmark. Together, these resources provide a reproducible baseline and a modular foundation for advancing similarity metrics, expanding the DQI taxonomy, and enabling knowledge-intensive reasoning in IoT data quality management.

Digital elevation models (DEMs) are fundamental data infrastructure that support a large range of applications in environmental modelling. The distribution of solar radiation, patterns of wetness and water movement, mobility of animals and evolution of soils are all intimately linked to topographic form. DEMs for the Australian continent have progressed from 1/10 th degree resolution in 1982 to 1/40 th degree in 1991, 9 th degree) in 1996 and now 1 second (about 30 m) derived from the NASA Shuttle Radar Topographic Mission (SRTM). Each improvement in resolution and accuracy enables new applications as more detail and structure are revealed. The 1 second SRTM-derived DEMs for Australia are now publicly available through the Geoscience Australia National Elevation Data Framework (NEDF) portal (http://nedf.ga.gov.au). The raw SRTM data has been processed through a series of steps to provide several products suitable for different purposes: • DEM-S is a bare-earth elevation model with stripes and voids removed, offsets due to trees treated and noise substantially reduced in flatter areas by adaptive smoothing, which is suitable for most applications where connectivity of flow is not the primary concern such as calculation of slopes and topographic position • DEM-H is a hydrologically conditioned elevation model derived from DEM-S with drainage enforcement using 1:250,000 scale stream lines, which is suitable for applications where hydrological connectivity is of primary concern such as calculation of contributing areas and catchment delineation • DEM is the pre-cursor to DEM-S before adaptive smoothing, which may be useful when the retention of detail is more important than smoothness. There is also a cleaned digital surface model (DSM) that is the precursor to DEM-S before tree offsets are removed, which is restricted to Australian government use only. Most users of DEMs need information about the shape of the landscape, not just height, so we are producing a set of standard derived products including slope, aspect, curvatures, topographic position, relief, contributing area and wetness index. These derived surfaces are key inputs for continental scale prediction of soil properties and ecological patterns. Predictions of a suite of key soil properties on a 3 second grid for the Australian continent, using the 1 second terrain layers, are being developed and will contribute to the GlobalSoilMap.net global grid of soil properties. Catchments and stream lines derived from DEM-H will form the basis for future versions of the Australian Hydrological Geospatial Fabric (http://www.bom.gov.au/water/geofabric/index.shtml). Digital elevation data continues to improve as measurement technology improves. Airborne lidar already provides high precision terrain data with vertical accuracy of 20 cm or better at 1 m horizontal resolution or better. This data is available in parts of Australia where the better accuracy justifies the high cost, such as urban areas, coastal lowlands, wetlands and floodplains. We need to develop ways of using high resolution data covering limited areas in combination with lower resolution data covering the continent. New global DEMs are being acquired and will replace SRTM in the years ahead. The TANDEM-X satellite radar system will potentially produce global elevations at 2-3 times better resolution and accuracy than SRTM, and other systems will follow. Higher resolutions bring further technical challenges, not least in data volume: each 1 second DEM product for Australia is a 40 GB dataset and finer resolutions will be correspondingly larger.

Species are distributed in predictable ways in geographic spaces. The three principal factors that determine geographic distributions of species are biotic interactions (B), abiotic conditions (A), and dispersal ability or mobility (M). A species is expected to be present in areas that are accessible to it and that contain suitable sets of abiotic and biotic conditions for it to persist. A species' probability of presence can be quantified as a combination of responses to B, A, and M via ecological niche modeling (ENM; also frequently referred to as species distribution modeling or SDM). This analytical approach has been used broadly in ecology and biogeography, as well as in conservation planning and decision-making, but commonly in the context of 'natural' settings. However, it is increasingly recognized that human impacts, including changes in climate, land cover, and ecosystem function, greatly influence species' geographic ranges. In this light, historical distinctions between natural and anthropogenic factors have become blurred, and a coupled human-natural landscape is recognized as the new norm. Therefore, B, A, and M (BAM) factors need to be reconsidered to understand and quantify species' distributions in a world with a pervasive signature of human impacts. Here, we present a framework, termed human-influenced BAM (Hi-BAM, for distributional ecology that (i) conceptualizes human impacts in the form of six drivers, and (ii) synthesizes previous studies to show how each driver modifies the natural BAM and species' distributions. Given the importance and prevalence of human impacts on species distributions globally, we also discuss implications of this framework for ENM/SDM methods, and explore strategies by which to incorporate increasing human impacts in the methodology. Human impacts are redefining biogeographic patterns; as such, future studies should incorporate signals of human impacts integrally in modeling and forecasting species' distributions.

Harmonizing National Topographic Data Using an Automated Quality Validation Process

There is a growing consensus that to effectively adapt to climate change, cities need user-friendly tools and reliable high-resolution biophysical and socio-economic data for analysis, mapping, modeling, and visualization. This study examines the availability of various types of information used in climate adaptation plans of 40 municipalities with a population of less than 300,000 people in the United States and France, probing into the choice and usage of relevant information by small municipalities. We argue that non-climatic spatial data, such as population demographic and socio-economic patterns, urban infrastructure, and environmental data must be integrated with climate tools and datasets to inform effective vulnerability assessment and equitable adaptation planning goals. Most climate adaptation plans examined in this study fail to address the existing structural inequalities and environmental injustices in urban infrastructure and land use. Their challenges include methodological and ideological barriers, data quality issues, and a lack of meaningful community connections. Adaptation methodological approaches should be reassessed in the context of much-needed societal transformation. Lessons learned from our studies offer valuable insights for the potential development of national and state-level climate adaptation information services for cities.

Data in project environments presents challenges and opportunities that project managers should exploit to the benefit of the project. Firstly, the sheer volume and variety of data generated throughout the project life cycle can overwhelm traditional management approaches, leading to issues of data governance, quality and integration. Furthermore, the dynamic nature of projects often leads to fragmented data silos, hindering collaboration and decision-making. This then raises the question of how project managers can use data to make better project-related decisions. Two focus groups consisting of industry experts were conducted, one in South Africa and the other in Canada. The industry experts all had extensive experience in managing digital transformation projects. These types of projects are notorious for the abundance of data generated. Various challenges were raised, such as the skills of the project manager and team, the quality of the data, the life cycle of the data and the technology associated with the data, for instance data lakes. Among the skills that the project team require are data and process mining as well as visualisation of the data to gain insights. Overcoming these challenges requires a holistic strategy that encompasses technological, organisational and cultural dimensions. A roadmap, based on the socio-technical system, was developed to assist project managers in gaining insights from project-generated data. The roadmap incorporates aspects such as infrastructure, people skills and the necessary processes. The roadmap can be used by project managers to engage with project-related data in a meaningful way, allowing them to gain insights from the data and make better decisions to the benefit of all stakeholders. Researchers can use this roadmap to engage in-depth with the theory and provide specific guidelines for the various components of the roadmap.

Quality data are not only relevant for successful Data Warehousing or Business Intelligence applications; they are also a precondition for efficient and effective use of Enterprise Resource Planning (ERP) systems. ERP professionals in all kinds of businesses are concerned with data quality issues, as a survey, conducted by the Institute of Information Systems at the University of Bern, has shown. This paper demonstrates, by using results of this survey, why data quality problems in modern ERP systems can occur and suggests how ERP researchers and practitioners can handle issues around the quality of data in an ERP software Environment.

Data quality issues in clinical trials can be caused by a variety of behaviors including fraud, misconduct, intentional or unintentional noncompliance, and significant carelessness. Regardless of how these behaviors are defined, they may compromise the validity of the study results. Reliable study results and quality data are needed to evaluate products for marketing approval and for decisions that are made on the use of medicine. This article focuses on detecting data quality issues, irrespective of origin or motive. Early detection of data quality issues are important so that corrective actions taken can be implemented during the conduct of the trial, recurrence can be prevented, and data quality can be preserved. A survey was distributed to TransCelerate member companies to assess current strategies for detecting and mitigating risks involving fraud and misconduct in clinical trials. A review of literature across many industries from 1985 to 2014 was conducted using multiple platforms. Eighteen TransCelerate member companies anonymously responded to the survey. All of the respondents had one or more existing strategies for fraud and misconduct detection. The literature search identified current practices and methodologies across many industries. TransCelerate recommends the creation of an integrated, multifaceted approach to proactively detect data quality issues. Detection methods should include a strategy tailored to the characteristics of the study. Some sponsors are taking advantage of more advanced methods and integrated processes and systems to proactively detect and address issues, relying on advances in technology to more efficiently review data in real time. Further research is underway to assess statistical data quality detection methodology in clinical trials.

Due to the dramatically increasing amount of available data, effective and scalable solutions for data organization and search are essential. Distributed solutions naturally provide promising alternatives to standard centralized approaches. With the computational power of thousands or millions of computers in clusters or P2P systems, the challenges that arise are manifold, ranging from efficient resource discovery to load balancing issues and distributed ranking problems. This 2008 Large Scale Distributed Systems for Information Retrieval Workshop (LSDS-IR08) is a forum for researchers to discuss these problems and to define new directions for the work on distributed information retrieval. This year's program features ten papers on emerging work in the areas of search in structured and unstructured networks, network organization schemes, issues of data quality, search for different data types, result ranking techniques and query routing algorithms. In addition, our keynote, entitled "Search Engine Architectures from Conventional to P2P," will be delivered by Torsten Suel of Polytechnic University in New York.

In this article, we have investigated how researchers use the data provided by the Brazilian Information System of Primary Care . We also searched, for the first time, studies that evaluated the quality and reliability of the information provided by the Primary Care Information System. An integrative review of the literature was performed using the keywords 'information systems, primary care and SIAB' on search databases, and 53 of 174 articles were selected. These publications were classified into two large subgroups: those using the Primary Care Information System as 'data source' and those that took it as the 'object of study'. The first group included 35 studies, 18 of which used demographic and social health data records, and nine described data about diseases, specifically hypertension and diabetes. These data were used by researchers for association with health indicators (20%) or comparison with other information systems (17%), sample or population calculus (9%), estimation of prevalence and characterization of the epidemiological profile of a population (26%) or, more generally, to carry out the assessment of health status (29%). The Primary Care Information System as the 'object of study' group included 18 works, describing the knowledge and practices of professionals in relation to the information system. These researchers pointed out issues in the process of production and information consolidation, mainly due to the lack of training and supervision of community health workers and bureaucratization of their work process. Although some issues in the quality of data provided by the Primary Care Information System were reported by researchers, these findings were not corroborated by two studies that assessed the reliability of information disclosed by this system. Despite changes in the Brazilian health policies, the issue of data quality in health information systems continues to be a challenge preventing data from being used for decision-making and knowledge production.

AI has become a disruptive technology in healthcare that is using the breakthroughs in computer science to improve the fields of diagnostics, treatment planning, patient monitoring, and medical research. In this review, the author discusses the historical development of AI starting with the early rule-based systems about modern machine learning, deep learning, and natural language processing applications. The most important areas of healthcare use are medical imaging, predictive analytics, personalized medicine and robotic-assisted surgery. Issues of data quality, interpretability, ethical issues, and regulation are mentioned, and novel trends like federated learning, generative AI, and human-AI collaboration are stated, and AI has potential to enhance patient outcomes and healthcare efficiency.

This paper analyzes various problems that appear while performing data mining. The issues of data quality are discussed. The main focus is set on feature selection and its influence on classification results. Feature selection, or discovery of an optimal data set is a process of removing features from the data set that are not useful in decision making, and leaving the most useful ones. The influence of feature selection is analyzed for different classification algorithms. They are applied on two different (in constitution) data sets to solve three problems of medical domain. Presented results show that there is no universal algorithm, whitch could help solving any problem, as well as each data set has its own optimal (sub)set suitable for the classification algorithm. Methodological recommendations to reach possibly optimal solution are given to perform clinical decision support.