jats:titleAbstract/jats:title jats:pThe speed and accuracy of new scientific discoveries – be it by humans or artificial intelligence – depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN). We have developed a simple application ontology based on OWL/RDF with mappings to standard schemas. We are employing the ontology to power data access services like resolvable URIs, SPARQL endpoints, JSON-LD web APIs and Neo4j-based knowledge graphs. We demonstrate how the proposed ontology and graph databases considerably improve search and access to interoperable and reusable biological knowledge (i.e. the FAIRness data principles)./jats:p

Introducing Our Comprehensive Global B2B Contact Data Solution

In today’s rapidly evolving business landscape, having access to accurate, comprehensive, and actionable information is not just an advantage—it’s a necessity. Introducing our Global B2B Contact Data Solution, meticulously crafted to empower businesses worldwide by providing them with the tools they need to connect, expand, and thrive in the global market.

What Distinguishes Our Data?

Our Global B2B Contact Data is a cut above the rest, designed with a laser focus on identifying and connecting with pivotal decision-makers. With a database of over 220 million meticulously verified contacts, our data goes beyond mere numbers. Each entry includes business emails and phone numbers that have been thoroughly vetted for accuracy, ensuring that your outreach efforts are both meaningful and effective. This data is a key asset for businesses looking to forge strong connections that are crucial for global expansion and success.

Unparalleled Data Collection Process

Our commitment to quality begins with our data collection process, which is rooted in a robust and reliable approach: - Dynamic Publication Sites: We draw data from ten dynamic publication sites, serving as rich sources for the continuous and real-time creation of our global database. - Contact Discovery Team: Complementing this is our dedicated research powerhouse, the Contact Discovery Team, which conducts extensive investigations to ensure the accuracy and relevance of each contact. This dual-sourcing strategy guarantees that our Global B2B Contact Data is not only comprehensive but also trustworthy, offering you the reliability you need to make informed business decisions.

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Our Global B2B Contact Data is designed with versatility in mind, making it an indispensable tool across a wide range of industries: - Finance: Enable precise targeting for investment opportunities, partnerships, and market expansion. - Manufacturing: Identify key players and suppliers in the global supply chain, facilitating streamlined operations and business growth. - Technology: Connect with innovators and leaders in tech to foster collaborations, drive innovation, and explore new markets. - Healthcare: Access critical decision-makers in healthcare for strategic partnerships, market penetration, and research collaborations. - Retail: Engage with industry leaders and stakeholders to enhance your retail strategies and expand your market reach. - Energy: Pinpoint decision-makers in the energy sector to explore new ventures, investments, and sustainability initiatives. - Transportation: Identify key contacts in logistics and transportation to optimize operations and expand into new territories. - Hospitality: Connect with executives and decision-makers in hospitality to drive business growth and market expansion. - And Beyond: Our data is applicable across virtually every industry, ensuring that no matter your sector, you have the tools needed to succeed.

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Our Global B2B Contact Data is not just a standalone resource—it’s a vital component of a larger data ecosystem that offers a panoramic view of the business landscape. By seamlessly integrating into our wider data collection framework, our Global B2B Contact Data enables you to: - Access Supplementary Insights: Gain additional valuable insights that complement your primary data, providing a well-rounded understanding of market trends, competitive dynamics, and global key players. - Informed Decision-Making: Whether you’re identifying new market opportunities, analyzing industry trends, or planning global expansion, our data equips you with the insights needed to make strategic, data-driven decisions.

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In today’s interconnected world, relationships are paramount. Our Global B2B Contact Data acts as a powerful conduit for establishing and nurturing these connections on a global scale. By honing in on decision-makers, our data ensures that you can effortlessly connect with the right individuals at the most opportune moments. Whether you’re looking to forge new partnerships, secure investments, or venture into uncharted B2B territories, our data empowers you to build meaningful and lasting business relationships.

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We understand that privacy and security are of utmost importance when it comes to handling data. That’s why we uphold the highest standards of privacy and security, ensuring that all data is managed ethically and in full compliance with global privacy regulations. Businesses can confidently leverage our data, knowing that it is handled with the utmost care and respect for legal requirements.

Continuous Enhancement for Superior Data Quality

Adaptability and continuous improvement are at the core of our ethos. We are committed to consistently enhancing our B2B C...

The Review extension for CKAN adds functionality to manage dataset and group review cycles within a CKAN instance. It introduces mechanisms for scheduling reviews, tracking their completion status, and notifying users when reviews are due, ensuring data remains current and relevant. By setting review intervals and associating them with datasets and groups, the extension helps maintain data quality and compliance. Key Features: Dataset and Group Review Logging: The extension adds package_review and group_review tables to the CKAN database, allowing administrators to store data about dataset and group review processes, including review dates and status. Organization-Level Review Interval Configuration: Introduces a 'Dataset Review Interval' field to organizations, enabling administrators to set a default review period for all datasets within these organizations. This facilitates consistent review scheduling across multiple datasets. Dataset-Specific Review Scheduling: Adds a 'Next Review' field to the dataset creation/editing interface, which defaults to the current date plus the organization’s 'Dataset Review Interval', offering a convenient way to schedule the first review for each dataset. Manual Review Marking: Provides a 'Mark as Reviewed' button on the dataset view page, which allows administrators to manually mark the dataset as reviewed, indicating that the review process has been completed. Automated Notifications via Activity Stream: Allows activity stream items to be created when packages are due to be reviewed, notifying users through a cron job that runs once a day. Technical Integration: The Review extension integrates with CKAN by adding new database tables and extending the dataset and organization schemas. Integration involves adding the 'review' plugin to the CKAN configuration file, configuring email settings for notifications, and setting up a cron job to trigger the notification process. This cron job uses the CKAN command-line interface to generate activity stream items. Benefits & Impact: By implementing the Review extension, organizations benefit from a structured approach to managing data quality within CKAN. Setting review intervals, tracking review completion, and automatically generating notifications significantly improve the upkeep of data. This ensures datasets remain up-to-date, accurate, and relevant, leading to improved decision-making and greater confidence in the information provided by CKAN.

Metabarcoding of environmental DNA (eDNA) is a powerful tool for describing biodiversity, such as finding keystone species or detecting invasive species in environmental samples. Continuous improvements in the method and the advances in sequencing platforms over the last decade have meant this approach is now widely used in biodiversity sciences and biomonitoring. For its general use, the method hinges on a correct identification of taxa. However, past studies have shown how this crucially depends on important decisions during sampling, sample processing, and subsequent handling of sequencing data. With no clear consensus as to the best practice, particularly the latter has led to varied bioinformatic approaches and recommendations for data preparation and taxonomic identification. In this study, using a large freshwater fish eDNA sequence dataset, we compared the frequently used zero-radius Operational Taxonomic Unit (zOTUs) approach of our raw reads and assigned it taxonomically i) in combination with publicly available reference sequences (open databases) or ii) with an OSU (Operational Sequence Units) database approach, using a curated database of reference sequences generated from specimen barcoding (closed database). We show both approaches gave comparable results for common species. However, the commonalities between the approaches decreased with read abundance and were thus less reliable and not comparable for rare species. The success of the zOTU approach depended on the suitability, rather than the size, of a reference database. Contrastingly, the OSU approach used reliable DNA sequences and thus often enabled species-level identifications, yet this resolution decreased with the recent phylogenetic age of the species. We show the need to include target group coverage, outgroups and full taxonomic annotation in reference databases to avoid misleading annotations that can occur when using short amplicon sizes as commonly used in eDNA metabarcoding studies. Finally, we make general suggestions to improve the construction and use of reference databases for metabarcoding studies in the future. Methods The data was collected under the framework of the federal water quality assessment in Switzerland. The data is generated from eDNA samples in Swiss rivers that are routinely surveyed. In spring 2019, 92 sites were sampled for eDNA with 4 replicates for each site. The eDNA filters were then extracted in a clean room. From these extracts, we used a nested PCR using the 12S Mifish primers to create an amplicon library, that was paired end sequenced. The 12S barcode is focused on the detection of fish communities. The libraries were prepared in-house and at the Genetic Diversity Center (user labs) at ETH Zurich.

Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone • ...

25+ Fuel Types like • Regular • Mid-Grade • Premium • Diesel • DEF • CNG •...

30+ Services and characteristics like • Carwash • Shop • Restaurant • Toilet • ATM • Pay at Pump •...

20+ Payment options • Cash • Visa • MasterCard • Fueling Cards • Google Pay • ...

Xavvy fuel is the leading source for Gas Station Location Data and Gasoline Price data worldwide and specialized in data quality and enrichment. Xavvy provides POI Data of gas stations at a high quality level for the United States. Next to base information like name/brand, address, geo-coordinates or opening hours, there are also detailed information about available fuel types, accessibility, special services, or payment options for each station. The level of information to be provided is highly customizable. One-time or regular data delivery, push or pull services, and any data format – we adjust to our customer’s needs.

Total number of stations per country or region, distribution of market shares among competitors or the perfect location for new gas stations, charging stations or hydrogen dispensers - our gas station data and gasoline price data provides answers to various questions and offers the perfect foundation for in-depth analyses and statistics. In this way, our data helps customers from various industries to gain more valuable insights into the fuel market and its development. Thereby providing an unparalleled basis for strategic decisions such as business development, competitive approach or expansion.

In addition, our data can contribute to the consistency and quality of an existing dataset. Simply map data to check for accuracy and correct erroneous data.

Especially if you want to display information about gas stations on a map or in an application, high data quality is crucial for an excellent customer experience. Therefore, our processing procedures are continuously improved to increase data quality:

• regular quality controls • Geocoding systems correct and specify geocoordinates • Data sets are cleaned and standardized • Current developments and mergers are taken into account • The number of data sources is constantly expanded to map different data sources against each other

Integrate the largest database of Retail Gas Station Location Data, Amenities and accurate Diesel and Gasoline Price Data in Europe and North America into your business. Check out our other Data Offerings available, and gain more valuable market insights on gas stations directly from the experts!

BackgroundThe use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and ‘validation’ analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository.MethodsWe used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework.ResultsAcross three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A ’FAIL’ occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%.ConclusionThe OMOP CDM’s widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

The Conservation Practice Effectiveness Database compiles information on the effectiveness of a suite of conservation practices. This database presents a compilation of data on the effectiveness of innovative practices developed to treat contaminants in surface runoff and tile drainage water from agricultural landscapes. Traditional conservation practices such as no-tillage and conservation crop rotation are included in the database, as well as novel practices such as drainage water management, blind inlets, and denitrification bioreactors. This will be particularly useful to conservation planners seeking new approaches to water quality problems associated with dissolved constituents, such as nitrate or soluble reactive phosphorus (SRP), and for researchers seeking to understand the circumstances in which such practices are most effective. Another novel feature of the database is the presentation of information on how individual conservation practices impact multiple water quality concerns. This information will be critical to enabling conservationists and policy makers to avoid (or at least be aware of) undesirable tradeoffs, whereby great efforts are made to improve water quality related to one resource concern (e.g., sediment) but exacerbate problems related to other concerns (e.g., nitrate or SRP). Finally, we note that the Conservation Practice Effectiveness Database can serve as a source of the soft data needed to calibrate simulation models assessing the potential water quality tradeoffs of conservation practices, including those that are still being developed. This database is updated and refined annually. Resources in this dataset:Resource Title: 2019 Conservation Practice Effectiveness (CoPE) Database. File Name: Conservation_Practice_Effectiveness_2019.xlsxResource Description: This version of the database was published in 2019.

IntroductionAccurately predicting patient outcomes is crucial for improving healthcare delivery, but large-scale risk prediction models are often developed and tested on specific datasets where clinical parameters and outcomes may not fully reflect local clinical settings. Where this is the case, whether to opt for de-novo training of prediction models on local datasets, direct porting of externally trained models, or a transfer learning approach is not well studied, and constitutes the focus of this study. Using the clinical challenge of predicting mortality and hospital length of stay on a Danish trauma dataset, we hypothesized that a transfer learning approach of models trained on large external datasets would provide optimal prediction results compared to de-novo training on sparse but local datasets or directly porting externally trained models.MethodsUsing an external dataset of trauma patients from the US Trauma Quality Improvement Program (TQIP) and a local dataset aggregated from the Danish Trauma Database (DTD) enriched with Electronic Health Record data, we tested a range of model-level approaches focused on predicting trauma mortality and hospital length of stay on DTD data. Modeling approaches included de-novo training of models on DTD data, direct porting of models trained on TQIP data to the DTD, and a transfer learning approach by training a model on TQIP data with subsequent transfer and retraining on DTD data. Furthermore, data-level approaches, including mixed dataset training and methods countering imbalanced outcomes (e.g., low mortality rates), were also tested.ResultsUsing a neural network trained on a mixed dataset consisting of a subset of TQIP and DTD, with class weighting and transfer learning (retraining on DTD), we achieved excellent results in predicting mortality, with a ROC-AUC of 0.988 and an F2-score of 0.866. The best-performing models for predicting long-term hospitalization were trained only on local data, achieving an ROC-AUC of 0.890 and an F1-score of 0.897, although only marginally better than alternative approaches.ConclusionOur results suggest that when assessing the optimal modeling approach, it is important to have domain knowledge of how incidence rates and workflows compare between hospital systems and datasets where models are trained. Including data from other health-care systems is particularly beneficial when outcomes are suffering from class imbalance and low incidence. Scenarios where outcomes are not directly comparable are best addressed through either de-novo local training or a transfer learning approach.

Abstract

The Quality Preschool for Ghana Impact Evaluation 2016, Midline survey (QP4G-ML 2016) was approved by the Strategic Impact Evaluation Fund (SIEF) of the World Bank on August 2015 in the Great Accra Region of Ghana. The official project name is called "Testing and scaling-up supply- and demand-side interventions to improve kindergarten educational quality in Ghana”, known as “Quality Preschool for Ghana (QP4G)”.

The project seeks to increase the quality of preschool education during the two years of universal Kindergarten (KG) in Ghana through intervening in the supply-side (i.e., teacher in-service training) and the demand side (i.e., increasing parental awareness for developmentally appropriate quality early education).

The primary goal of the impact evaluation is to test the efficacy of a potentially scalable (8-day) in-service teacher training to improve the quality of KG teacher practices and interactions with children and to improve children’s development, school readiness and learning in both private and public preschools in the Greater Accra Region of Ghana. Additional goals of this evaluation are: to test the added value of combining a scalable (low-cost) parental awareness intervention with teacher in-service training; to compare implementation challenges in public and private schools; and to examine several important sources of potential heterogeneity of impact, primarily impacts in public vs. private schools.

The current submission is for the Midline Survey, conducted with 3 types of respondents across two phases – School survey and Caregiver [household] surveys. The school survey was conducted from May to July 2016 and consisted of collecting the following data: (a) direct assessments of children’s school readiness, (b) surveys of KG teachers, (c) direct observation of inventory of facilities within KG classrooms [environmental scan]; videotaping of KG classroom processes, teaching, and learning (not being submitted); as well as video coding of KG classroom video recordings using Teacher Instructional Practices and Processes Systems (instrument not being submitted). The caregiver survey was conducted via phone from August to September 2016 on primary caregivers of KG children. The caregiver survey sought information on caregivers’ background, poverty status, involvement or participation in school and home activities, and perception about ECD. Overall, the Midline Survey was conducted from May to September 2016 for all respondents.

Geographic coverage

Urban and Peri-Urban Districts, Greater Accra Region

Analysis unit

Units of analysis include individuals (KG teachers, children, caregivers), KG classrooms and preschools.

Universe

The survey universe is 6 poor districts in the Greater Accra Region. We sampled 240 schools, 108 public (Govt.) schools and 132 private schools. The population of interest is KG teachers and children in KG 1 and KG 2 classrooms in these schools, as well as the caregivers of sampled students.

Kind of data

Sample survey data [ssd]

Sampling procedure

This impact evaluation applies a cluster-randomized design. Eligible schools were randomly selected to participate in the study. The eligible population was schools with KG 1 and KG 2 classrooms (the two years of universal preprimary education) in six districts in the Greater Accra Region. In these six districts, we have sampled 240 schools; 108 public schools and 132 private schools in total.

The unit of randomization for this randomized control trial (RCT) is schools, whereby eligible schools (stratified by public and private sector schools) are randomly assigned to: (1) in-service teacher-training program only; (2) in-service teacher-training program plus parental awareness program; or (3) control (current standard operating) condition.

The sampling frame for this study was based on data in the Education Management Information System (EMIS) from the Ghana Education Service. This data was verified in a 'school listing exercise' conducted in May 2015.

Sample selection was done in four stages: The first stage involved purposive selection of six districts within the region based on two criteria: (a) most disadvantaged (using UNICEF's District League Table scores, out of sixteen total districts); and (b) close proximity to Accra Metropolitan for travel for the training of the KG teachers. The six selected municipals were La Nkwantanang-Madina Municipal, Ga Central Municipal, Ledzokuku-Krowor Municipal, Adentan Municipal, Ga South Municipal and Ga East Municipal.

The second stage involved the selection of public and private schools from each of the selected districts in the Accra region. We found 678 public and private schools (schools with kindergarten) in the EMIS database. Of these 361 schools were sampled randomly (stratified by district and school type) for the school listing exercise, done in May 2015. This was made up of 118 public schools and 243 private schools. The sampling method used for the school listing exercise was based on two approaches depending on the type of school. For the public schools, the full universe of public schools (i.e., 118) were included in the school listing exercise. However, private schools were randomly sampled using probability proportional to the size of the private schools in each district. Specifically, the private schools were sampled in each district proportionate to the total number of district private schools relative to the total number of private schools. In so doing, one school from the Ga South Municipal was removed and added to Ga Central so that all districts have a number of private schools divisible by three. This approach yielded 122 private schools. Additionally, 20 private schools were randomly selected from each of the districts (i.e., based on the remaining list of private schools in each district following from the first selection) to serve as replacement lists. The replacement list was necessary given the potential refusals from the private schools. There were no replacement lists for the public schools since all public schools would automatically qualify for participation.

The third stage involved selecting the final sample for the evaluation using the sampling frame obtained through the listing exercise. A total of 240 schools were randomly selected, distributed by district and sector. Schools were randomized into treatment groups after the first round of baseline data collection was completed.

The survey respondents were sampled using different sampling techniques: a. KG teachers: The research team sampled two KG teachers from each school; one from KG1 and KG2. KG teachers were sampled using purposive sampling method. In schools where there were more than two KG classes, the KG teachers from the "A" stream were selected. For the treatment schools, all KG teachers were invited to participate in the teacher training program. b. KG child-caregiver pair: The research team sampled KG children and their respective caregivers using simple random sampling method. Fifteen KG children-caregivers pair were sampled from each school. For schools with less than 15 KG children (8 from KG1, 7 from KG2 where possible), all KG children were included in the survey. KG children were selected from the same class as the selected KG teacher. The survey team used the class register to randomly select KG children who were present on the day of the school visit. Sampling was not stratified by gender or age. The caregivers of these selected child respondents were invited to participate in the survey. The research team sought informed consent from the school head teacher, caregivers, as well as child respondents.

Mode of data collection

Other [oth]

Research instrument

Data were collected at Midline Survey using structured questionnaires or forms.

Child Direct Assessment: The KG Child Assessment was conducted using the International Development and Early Learning Assessment (IDELA) tool designed by Save the Children. IDELA was adapted based on extensive pre-testing and piloting by different members of the evaluation team. The adapted version measured five indicators of ECD. The indicators were early numeracy skills, language/literacy skills and development, physical well-being and motor development, socio-emotional development, and approaches to learning. IDELA contained 28 items. In addition, one task was added – the Pencil Tap – to assess executive function skills. Apart from the English language, IDELA was translated and administered into three local languages, namely, Twi, Ga, and Ewe. These local language versions had gone through rigorous processes of translation and back translation. The IDELA tool has not been shared as Save the Children have proprietary rights over this.

KG Class Environmental Scan: The KG classroom observation involved taking inventories of the KG classrooms [environmental scan] and conducting video recordings of the classroom processes. The KG Class Environmental Scan tool was designed to take inventories of the facilities in the KG classrooms. The classroom video recordings have not been shared as they contain PIIs.

TIPPS: The video recordings taken during the classroom observations were coded using an early childhood education adapted version of Teacher Instructional Practices and Processes Systems (TIPPS). Seidman, Raza, Kim, and McCoy (2014) of New York University developed the TIPPS instrument. TIPPS observes nineteen key concepts of teacher practices and classroom processes that influence children’s cognitive and social-emotional development. The concept sheet was used to code the kindergarten classroom videos. The TIPPS tool has not been shared as

Database Testing Tool Market Outlook

The global database testing tool market size was valued at approximately USD 3.2 billion in 2023 and is expected to reach USD 7.8 billion by 2032, growing at a CAGR of 10.5% during the forecast period. Factors such as the increasing volume of data generated by organizations and the need for robust data management solutions are driving the market growth.

One of the primary growth factors for the database testing tool market is the exponential increase in data generation across various industries. The advent of big data, IoT, and other data-intensive technologies has resulted in massive amounts of data being generated daily. This surge in data necessitates the need for efficient testing tools to ensure data accuracy, integrity, and security, which in turn drives the demand for database testing tools. Moreover, as businesses increasingly rely on data-driven decision-making, the importance of maintaining high data quality becomes paramount, further propelling market growth.

Another significant factor contributing to the growth of this market is the increasing adoption of cloud computing and cloud-based services. Cloud platforms offer scalable and flexible solutions for data storage and management, making it easier for companies to handle large volumes of data. As more organizations migrate to the cloud, the need for effective database testing tools that can operate seamlessly in cloud environments becomes critical. This trend is expected to drive market growth as cloud adoption continues to rise across various industries.

In the realm of software development, the use of Software Testing Tools is becoming increasingly critical. These tools are designed to automate the testing process, ensuring that software applications function correctly and meet specified requirements. By employing Software Testing Tools, organizations can significantly reduce the time and effort required for manual testing, allowing their teams to focus on more strategic tasks. Furthermore, these tools help in identifying bugs and issues early in the development cycle, thereby reducing the cost and time associated with fixing defects later. As the complexity of software applications continues to grow, the demand for advanced Software Testing Tools is expected to rise, driving innovation and development in this sector.

Additionally, regulatory compliance and data governance requirements are playing a crucial role in the growth of the database testing tool market. Governments and regulatory bodies across the globe have implemented stringent data protection and privacy laws, compelling organizations to ensure that their data management practices adhere to these regulations. Database testing tools help organizations meet compliance requirements by validating data integrity, security, and performance, thereby mitigating the risk of non-compliance and associated penalties. This regulatory landscape is expected to further boost the demand for database testing tools.

On the regional front, North America is anticipated to hold a significant share of the database testing tool market due to the presence of major technology companies and a robust IT infrastructure. The region's early adoption of advanced technologies and a strong focus on data management solutions contribute to its market dominance. Europe is also expected to witness substantial growth, driven by stringent data protection regulations such as GDPR and the increasing adoption of cloud services. The Asia Pacific region is projected to exhibit the highest growth rate during the forecast period, owing to the rapid digital transformation, rising adoption of cloud computing, and growing awareness of data quality and security among enterprises.

Type Analysis

The database testing tool market is segmented by type into manual testing tools and automated testing tools. Manual testing tools involve human intervention to execute test cases and analyze results, making them suitable for small-scale applications or projects with limited complexity. However, the manual testing approach can be time-consuming and prone to human errors, which can affect the accuracy and reliability of the test results. Despite these limitations, manual testing tools are still favored in scenarios where precise control and detailed observations are required.

Automated testing tools, on the other hand, have gained significant traction due to their ability to execute a large

In order to foster a modern economic system and facilitate high-quality economic development, it is crucial to establish a conducive business environment. Undoubtedly, the evaluation of the business environment for enterprises constitutes a prominent area of research. Nevertheless, ensuring the authenticity and security of the raw data sources provided by participating enterprises poses a challenge, thereby compromising the accuracy of the evaluation. To tackle this issue, an enterprise composite blockchain construction method for business environment is proposed in this paper, which stores the raw data of enterprises by the means of hybrid on-chain and off-chain. Initially, the enhanced hash function SHA256 is introduced to encrypt the raw data of enterprises. The encrypted data is subsequently stored in an off-chain Level DB database, which is based on non-volatile memory. This approach effectively alleviates the burden on communication and storage. Secondly, a composite storage strategy on-chain is adopted: the key values from the Level DB are stored in the DAG-based Conflux public blockchain, while the enterprise state data is stored in the consortium blockchain, so as to provide trusted evidence of business environment evaluation data. Finally, it is demonstrated through a large number of experimental comparisons that the enterprise composite blockchain construction method proposed in this paper exhibits better read and write performance, lower storage efficiency and storage overhead, and outperforms both the before-improved Level DB database and existing blockchain storage models.

The quality of health care remains generally poor across primary health care settings, especially in low- and middle-income countries where tertiary care tends to take up much of the limited resources despite primary health care being the first (and often the only) point of contact with the health system for nearly 80 per cent of people in these countries. Evidence is needed on barriers and enablers of quality improvement initiatives. This systematic review sought to answer the question: What are the enablers of and barriers to quality improvement in primary health care in low- and middle-income countries? It adopted an integrative review approach with narrative evidence synthesis, which combined qualitative and mixed methods research studies systematically. Using a customized geographic search filter for LMICs developed by the Cochrane Collaboration, Scopus, Academic Search Ultimate, MEDLINE, CINAHL, PSYCHINFO, EMBASE, ProQuest Dissertations and Overton.io (a new database for LMIC literature) were searched in January and February 2023, as were selected websites and journals. 7,077 reports were retrieved. After removing duplicates, reviewers independently screened titles, abstracts and full texts, performed quality appraisal and data extraction, followed by analysis and synthesis. 50 reports from 47 studies were included, covering 52 LMIC settings. Six themes related to barriers and enablers of quality improvement were identified and organized using the model for understanding success in quality (MUSIQ) and the consolidated framework for implementation research (CFIR). These were: microsystem of quality improvement, intervention attributes, implementing organization and team, health systems support and capacity, external environment and structural factors, and execution. Decision makers, practitioners, funders, implementers, and other stakeholders can use the evidence from this systematic review to minimize barriers and amplify enablers to better the chances that quality improvement initiatives will be successful in resource-limited settings. PROSPERO registration: CRD42023395166.

This dataset (published in EPSG:4326 - WGS84) provides polygon data for local authority Green Belt boundaries. Local authorities digitise areas of land designated as Green Belt and send the Green Belt boundaries to DLUHC. The local authority Green Belt boundaries are merged and quality assured by DLUHC and are mapped against OS and ONS Local Authority District (Mean High Water mark) boundaries for the corresponding period. This improves the data quality, removing overlaps between Local Authority Green Belt boundaries and provides consistent approach to area calculations and in the delimiting of land designated as Green Belt where it meets coastal or estuarine areas. Source: Office for National Statistics licensed under the Open Government Licence v.3.0; Contains OS data © Crown copyright and database right 2023.

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.Terrestrial and Freshwater Data• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels, such that the spatial features of CPAD correspond to ownership boundaries. • The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity. Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership. • The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such, boundaries represented within it may not align with other data sources. • Numerous datasets representing designated boundaries for entities such as National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas, and others, were downloaded from publicly available sources, typically hosted by the managing agency.Methodology1. CPAD and CCED represent the most accurate location and ownership information for parcels in California which contribute to the preservation of open space and cultural and biological resources.2. Superunits are collections of parcels (Holdings) within CPAD which share a name, manager, and access policy. Most Superunits are also managed with a generally consistent strategy for biodiversity conservation. Examples of Superunits include Yosemite National Park, Giant Sequoia National Monument, and Anza-Borrego Desert State Park. 3. Some Superunits, such as those owned and managed by the Bureau of Land Management, U.S. Forest Service, or National Park Service , are intersected by one or more designations, each of which may have a distinct management emphasis with regards to biodiversity. Examples of such designations are Wilderness Areas, Wild and Scenic Rivers, or National Monuments.4. CPAD Superunits and CCED easements were intersected with all designation boundary files to create the operative spatial units for conservation analysis, henceforth 'Conservation Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit. 5. Each Conservation Unit was intersected with the PAD-US dataset in order to determine the management emphasis with respect to biodiversity, i.e., the GAP code. Because PAD-US is national in scope and not specifically parcel aligned with California assessors' surveys, a direct spatial extraction of GAP codes from PAD-US would leave tens of thousands of GAP code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single GAP code was uniformly assigned that code. Additionally, the total area of GAP codes 1 and 2 were summed for the remaining uncoded Conservation Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2. 6. Subsequent to this stage of analysis, certain Conservation Units remained uncoded, either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset. 7. These uncoded Conservation Units were then broken down into their constituent, finer resolution Holdings, which were then analyzed according to the above workflow. 8. Areas remaining uncoded following the two-step process of coding at the Superunit and then Holding levels were assigned a GAP code of 4. This is consistent with the definition of GAP Code 4: areas unknown to have a biodiversity management focus. 9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.Tracking Conserved AreasThe total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing improvements in our understanding of existing biodiversity conservation efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data. CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review. The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.Terrestrial and Freshwater Data• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels which comprise the spatial features of CPAD, generally corresponding to ownership boundaries. • The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity. Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership. •The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such, boundaries represented within it may not align with other data sources. • Numerous datasets representing designated boundaries for entities such as National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas, and others, were downloaded from publicly available sources, typically hosted by the managing agency.Methodology1.CPAD and CCED represent the most accurate location and ownership information for parcels in California which contribute to the preservation of open space and cultural and biological resources.2. Superunits are collections of parcels (Holdings) within CPAD which share a name, manager, and access policy. Most Superunits are also managed with a generally consistent strategy for biodiversity conservation. Examples of Superunits include Yosemite National Park, Giant Sequoia National Monument, and Anza-Borrego Desert State Park. 3. Some Superunits, such as those owned and managed by the Bureau of Land Management, U.S. Forest Service, or National Park Service , are intersected by one or more designations, each of which may have a distinct management emphasis with regards to biodiversity. Examples of such designations are Wilderness Areas, Wild and Scenic Rivers, or National Monuments.4. CPAD Superunits and CCED easements were intersected with all designation boundary files to create the operative spatial units for conservation analysis, henceforth 'Conservation Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit. 5. Each Conservation Unit was intersected with the PAD-US dataset in order to determine the management emphasis with respect to biodiversity, i.e., the GAP code. Because PAD-US is national in scope and not specifically parcel aligned with California assessors' surveys, a direct spatial extraction of GAP codes from PAD-US would leave tens of thousands of GAP code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single GAP code was uniformly assigned that code. Additionally, the total area of GAP codes 1 and 2 were summed for the remaining uncoded Conservation Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2. 6.Subsequent to this stage of analysis, certain Conservation Units remained uncoded, either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset. 7.These uncoded Conservation Units were then broken down into their constituent, finer resolution Holdings, which were then analyzed according to the above workflow. 8. Areas remaining uncoded following the two-step process of coding at the Superunit and then Holding levels were assigned a GAP code of 4. This is consistent with the definition of GAP Code 4: areas unknown to have a biodiversity management focus. 9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.10. PAD-US is a principal data source for understanding the spatial distribution of GAP coded lands, but it is national in scope, and may not always be the most current source of data with respect to California holdings. GreenInfo Network, which develops and maintains the CPAD and CCED datasets, has taken a lead role in establishing communication with land stewards across California in order to make GAP attribution of these lands as current and accurate as possible. The tabular attribution of these datasets is analyzed in addition to PAD-US in order to understand whether a holding may be considered conserved. Tracking Conserved Areas The total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing improvements in our understanding of existing biodiversity conservation efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data. CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review.The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

Soil surface ESP is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Southern Gulf Water Resource Assessment (SOGWRA) through the digital soil mapping process (DSM). This soil surface exchangeable sodium percent (ESP) raster data represents modelled data of ESP of the soil surface (<0.10m depth) expressed as a percent and is derived from analysed site data and environmental covariates. Soil surface ESP is a parameter used in land suitability assessments of factors impacting water infiltration and potential erosion eg high ESP soils have reduced surface infiltration of rainfall and irrigation water. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO SOGWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO SOGWRA published report ‘Soils and land suitability for the Southern Gulf catchments’. A technical report from the CSIRO Southern Gulf Water Resource Assessment to the Government of Australia. The Southern Gulf Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Southern Gulf catchments NT and Qld as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: This soil surface ESP dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO SOGWRA published reports and in particular ' Soils and land suitability for the Southern Gulf catchments’. A technical report from the CSIRO Southern Gulf Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil surface ESP Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and R squared results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For continuous attributes the method for estimating reliability is the Coefficient of Variation. This data is supplied. Method 3: A workshop was conducted in March 2023 to review DSM soil attribute and land suitability products and facilitated an alternative to the field external validation carried out in other northern Australia water resource assessments. Stakeholders from the NT and Qld jurisdictions reviewed, evaluated and discussed the soundness of the data and processes. The workshop desk top assessment approach provided recommendations for acceptance, improvement and re-modelling of attributes based on expert knowledge and understanding of the soil distribution and landscape in the study area and available data.

In order to foster a modern economic system and facilitate high-quality economic development, it is crucial to establish a conducive business environment. Undoubtedly, the evaluation of the business environment for enterprises constitutes a prominent area of research. Nevertheless, ensuring the authenticity and security of the raw data sources provided by participating enterprises poses a challenge, thereby compromising the accuracy of the evaluation. To tackle this issue, an enterprise composite blockchain construction method for business environment is proposed in this paper, which stores the raw data of enterprises by the means of hybrid on-chain and off-chain. Initially, the enhanced hash function SHA256 is introduced to encrypt the raw data of enterprises. The encrypted data is subsequently stored in an off-chain Level DB database, which is based on non-volatile memory. This approach effectively alleviates the burden on communication and storage. Secondly, a composite storage strategy on-chain is adopted: the key values from the Level DB are stored in the DAG-based Conflux public blockchain, while the enterprise state data is stored in the consortium blockchain, so as to provide trusted evidence of business environment evaluation data. Finally, it is demonstrated through a large number of experimental comparisons that the enterprise composite blockchain construction method proposed in this paper exhibits better read and write performance, lower storage efficiency and storage overhead, and outperforms both the before-improved Level DB database and existing blockchain storage models.

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