Recent developments include: January 2022: IBM and Francisco Partners disclosed the execution of a definitive contract under which Francisco Partners will purchase medical care information and analytics resources from IBM, which are currently part of the IBM Watson Health business., October 2021: Informatica LLC announced an important cloud storage agreement with Google Cloud in October 2021. This collaboration allows Informatica clients to transition to Google Cloud as much as twelve times quicker. Informatica's Google Cloud Marketplace transactable solutions now incorporate Master Data Administration and Data Governance capabilities., Completing a unit of labor with incorrect data costs ten times more estimates than the Harvard Business Review, and finding the correct data for effective tools has never been difficult. A reliable system may be implemented by selecting and deploying intelligent workflow-driven, self-service options tools for data quality with inbuilt quality controls.. Key drivers for this market are: Increasing demand for data quality: Businesses are increasingly recognizing the importance of data quality for decision-making and operational efficiency. This is driving demand for data quality tools that can automate and streamline the data cleansing and validation process.

Growing adoption of cloud-based data quality tools: Cloud-based data quality tools offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness. This is driving the adoption of cloud-based data quality tools across all industries.

Emergence of AI-powered data quality tools: AI-powered data quality tools can automate many of the tasks involved in data cleansing and validation, making it easier and faster to achieve high-quality data. This is driving the adoption of AI-powered data quality tools across all industries.. Potential restraints include: Data privacy and security concerns: Data privacy and security regulations are becoming increasingly stringent, which can make it difficult for businesses to implement data quality initiatives.

Lack of skilled professionals: There is a shortage of skilled data quality professionals who can implement and manage data quality tools. This can make it difficult for businesses to achieve high-quality data.

Cost of data quality tools: Data quality tools can be expensive, especially for large businesses with complex data environments. This can make it difficult for businesses to justify the investment in data quality tools.. Notable trends are: Adoption of AI-powered data quality tools: AI-powered data quality tools are becoming increasingly popular, as they can automate many of the tasks involved in data cleansing and validation. This makes it easier and faster to achieve high-quality data.

Growth of cloud-based data quality tools: Cloud-based data quality tools are becoming increasingly popular, as they offer several advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness.

Focus on data privacy and security: Data quality tools are increasingly being used to help businesses comply with data privacy and security regulations. This is driving the development of new data quality tools that can help businesses protect their data..

The data set contains all rejuvenation units collected within the framework of forest quality management measures. For each rejuvenation unit, the year of justification of the rejuvenation unit, the type of rejuvenation measure, the type of tree used, the origin of the seed or plants, type of justification procedure, type of rejuvenation type, extent of rejuvenation, fencing and status of the rejuvenation unit, etc. are indicated.

Enterprise Data Management Market Outlook 2032

The global enterprise data management market size was USD 96.2 Billion in 2023 and is projected to reach USD 240.2 Billion by 2032, expanding at a CAGR of 10.7% during 2024–2032. The market growth is attributed to the growing demand for efficient data management systems.



The global EDM market is witnessing a rising trend, driven by several factors. The increasing volume of business data, coupled with the growing need for data security and privacy, is propelling the demand for robust data management solutions. Furthermore, the advent of advanced technologies such as artificial intelligence, machine learning, and big data analytics is opening new avenues for the growth of the market.

The ongoing digital transformation across various industries is creating a need for effective data management solutions. Moreover, the growing adoption of cloud-based services and solutions is expected to further boost the market. Businesses are increasingly recognizing the value of data as a strategic asset and are investing in EDM solutions to harness its potential, thereby driving market growth.

Impact of Artificial Intelligence (AI) on Enterprise Data Management Market

Artificial Intelligence has a positive impact on the enterprise data management market. It streamlines data processing, enhances data quality, and accelerates decision-making processes. AI-powered tools analyze vast amounts of data, identify patterns, and generate insights, thereby reducing human error and improving efficiency.

Enterprise Data Management Market Dynamics

Major Drivers

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Dit bestand bevat de data die zijn verzameld in het kader van het proefschrift van Sanne Elling: ‘Evaluating website quality: Five studies on user-focused evaluation methods’.Summary:The benefits of evaluating websites among potential users are widely acknowledged. There are several methods that can be used to evaluate the websites’ quality from a users’ perspective. In current practice, many evaluations are executed with inadequate methods that lack research-based validation. This thesis aims to gain more insight into evaluation methodology and to contribute to a higher standard of website evaluation in practice. A first way to evaluate website quality is measuring the users’ opinions. This is often done with questionnaires, which gather opinions in a cheap, fast, and easy way. However, many questionnaires seem to miss a solid statistical basis and a justification of the choice of quality dimensions and questions. We therefore developed the ‘Website Evaluation Questionnaire’ (WEQ), which was specifically designed for the evaluation of governmental websites. In a study in online and laboratory settings the WEQ has proved to be a valid and reliable instrument. A way to gather more specific user opinions, is inviting participants to review website pages. Participants provide their comments by clicking on a feedback button, marking a problematic segment, and formulating their feedback.There has been debate about the extent to which users are able to provide relevant feedback. The results of our studies showed that participants were able to provide useful feedback. They signalled many relevant problems that indeed were experienced by users who needed to find information on the website. Website quality can also be measured during participants’ task performance. A frequently used method is the concurrent think-aloud method (CTA), which involves participants who verbalize their thoughts while performing tasks. There have been doubts on the usefulness and exhaustiveness of participants’ verbalizations. Therefore, we have combined CTA and eye tracking in order to examine the cognitive processes that participants do and do not verbalize. The results showed that the participants’ verbalizations provided substantial information in addition to the directly observable user problems. There was also a rather high percentage of silences (27%) during which interesting observations could be made about the users’ processes and obstacles. A thorough evaluation should therefore combine verbalizations and (eye tracking) observations. In a retrospective think-aloud (RTA) evaluation participants verbalize their thoughts afterwards while watching a recording of their performance. A problem with RTA is that participants not always remember the thoughts they had during their task performance. We therefore complemented the dynamic screen replay of their actions (pages visited and mouse movements) with a dynamic gaze replay of the participants’ eye movements.Contrary to our expectations, no differences were found between the two conditions. It is not possible to draw conclusions on the single best method. The value of a specific method is strongly influenced by the goals and context of an evaluation. Also, the outcomes of the evaluation not only depend on the method, but also on other choices during the evaluation, such as participant selection, tasks, and the subsequent analysis. Date Submitted: 2013-09-10

Inclusion and exclusion criteria with justification.

In silico absorption modeling has been performed, to assess the impact of in vitro dissolution on in vivo performance for ZURAMPIC (lesinurad) tablets. The dissolution profiles of lesinurad tablets generated using the quality control method were used as an input to a GastroPlus model to estimate in vivo dissolution in the various parts of the GI tract and predict human exposure. A model was set up, which accounts for differences of dosage form transit, dissolution, local pH in the GI tract, and fluid volumes available for dissolution. The predictive ability of the model was demonstrated by confirming that it can reproduce the Cmax observed for independent clinical trial. The model also indicated that drug product batches that pass the proposed dissolution specification of Q = 80% in 30 min are anticipated to be bioequivalent to the clinical reference batch. To further explore the dissolution space, additional simulations were performed using a theoretical dissolution profile below the proposed specification. The GastroPlus modeling indicates that such a batch will also be bioequivalent to standard clinical batches despite having a dissolution profile, which would fail the proposed dissolution specification of Q = 80% in 30 min. This demonstrates that the proposed dissolution specification sits comfortably within a region of dissolution performance where bioequivalence is anticipated and is not near an edge of failure for dissolution, providing additional confidence to the proposed specifications. Finally, simulations were performed using a virtual drug substance batch with a particle size distribution at the limit of the proposed specification for particle size. Based on these simulations, such a batch is also anticipated to be bioequivalent to clinical reference, demonstrating that the proposed specification limits for particle size distribution would give products bioequivalent to the pivotal clinical batches.

Descriptive compassion fatigue and professional quality of life.

Pharmaceutical and Biotechnology Environmental Monitoring Market Outlook 2032

The global pharmaceutical and biotechnology environmental monitoring market size was USD 1.04 Billion in 2023 and is projected to reach USD 1.86 Billion by 2032, expanding at a CAGR of 6.69% during 2024–2032. The market growth is attributed to therising incidences of contamination-related recalls and the rising adoption of cleanroom technology.



Growing awareness of the importance of environmental monitoring is projected to boost the market. Pharmaceutical and biotechnology companies recognize the critical role of a controlled environment in maintaining product quality and patient safety. This awareness has led to the adoption of sophisticated monitoring systems that provide real-time data on environmental conditions. Enhanced focus on quality assurance and risk management practices further underscores the necessity for robust monitoring solutions, contributing to propelling the market.

Rising investments in research and development are likely to propel the market. Pharmaceutical and biotechnology companies are allocating substantial resources to develop next-generation environmental monitoring solutions. These investments aim to address emerging challenges, such as the need for higher sensitivity, faster response times, and greater data integration capabilities. The ongoing R&D efforts are expected to yield innovative products that meet the evolving demands of the industry, thereby fueling the market.

Impact of Artificial Intelligence (AI) in Pharmaceutical and Biotechnology Environmental Monitoring Market

Artificial Intelligencehas a positive impact on pharmaceutical and biotechnology environmental monitoring market.AI-driven solutions enhance the accuracy and efficiency of monitoring systems by enabling real-time data analysis and predictive analytics. Companies leverage AI to identify potential contamination risks and deviations from standard operating conditions, allowing for immediate corrective actions.



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BackgroundRemote self-administered visual acuity (VA) tests have the potential to allow patients and non-specialists to assess vision without eye health professional input. Validation in pragmatic trials is necessary to demonstrate the accuracy and reliability of tests in relevant settings to justify deployment. Here, published pragmatic trials of these tests were synthesised to summarise the effectiveness of available options and appraise the quality of their supporting evidence.MethodsA systematic review was undertaken in accordance with a preregistered protocol (CRD42022385045). The Cochrane Library, Embase, MEDLINE, and Scopus were searched. Screening was conducted according to the following criteria: (1) English language; (2) primary research article; (3) visual acuity test conducted out of eye clinic; (4) no clinical administration of remote test; (5) accuracy or reliability of remote test analysed. There were no restrictions on trial participants. Quality assessment was conducted with QUADAS-2.ResultsOf 1227 identified reports, 10 studies were ultimately included. One study was at high risk of bias and two studies exhibited concerning features of bias; all studies were applicable. Three trials—of DigiVis, iSight Professional, and Peek Acuity—from two studies suggested that accuracy of the remote tests is comparable to clinical assessment. All other trials exhibited inferior accuracy, including conflicting results from a pooled study of iSight Professional and Peek Acuity. Two studies evaluated test-retest agreement—one trial provided evidence that DigiVis is as reliable as clinical assessment. The three most accurate tests required access to digital devices. Reporting was inconsistent and often incomplete, particularly with regards to describing methods and conducting statistical analysis.ConclusionsRemote self-administered VA tests appear promising, but further pragmatic trials are indicated to justify deployment in carefully defined contexts to facilitate patient or non-specialist led assessment. Deployment could augment teleophthalmology, non-specialist eye assessment, pre-consultation triage, and autonomous long-term monitoring of vision.