Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

Background Acute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis. This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death.

The most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases. ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy. The highly granular synthetic data for over 900 patients with ACS provide the following key parameters to support critical research into this condition:

1. Patient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities)
2. Physiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness)
3. Muscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase)
4. Blood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers)

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details.

Available supplementary data: ACS cohort, Matched controls; ambulance, OMOP data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Ovaj skup podataka uključuje finansijske izvještaje, račune i blokade, te nekretnine. Podaci uključuju prihode, rashode, dobit, imovinu, obaveze i informacije o nekretninama u vlasništvu kompanije. Finansijski podaci, finansijski sažetak, sažetak kompanije, preduzetnik, zanatlija, udruženje, poslovni subjekti.

This is the code and processed data related to the publication:

Dong, Y., Zhang, L., Farah, H., Zgonnikov, A., & van Arem, B. (2023). Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection. arXiv preprint arXiv:2312.04610. https://arxiv.org/abs/2312.04610

The original data is from https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-Dataset

The codes make use of open-sourced implementation of HELM and other semi-supervised learning algorithms.

After setting up the folder and fetching the data, one can simply run the code with the specific function (identified by their names) get the relevant results.

Details about the implementation are demonstrated in the paper.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Detecting abnormal driving behaviour is critical for road traffic safety and the evaluation of drivers' behaviour. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behaviour detection (also referred to as anomalies). Most existing ML-based detectors rely on supervised methods, which require substantial labelled data. However, ground truth labels are not always available in the real world, and labelling large amounts of data is tedious. Thus, there is a need to explore unsupervised or semi-supervised methods to make the anomaly detection process more feasible and efficient. To fill this research gap, this study analyzes large-scale real-world data revealing several abnormal driving behaviours (e.g., sudden acceleration, rapid lane-changing) and develops a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method using partly labelled data to accurately detect the identified abnormal driving behaviours. Moreover, previous ML-based approaches predominantly utilized basic vehicle motion features (e.g., velocity and acceleration) to label and detect abnormal driving behaviours, while this study seeks to introduce event-level safety indicators as input features for ML models to improve detection performance. Results from extensive experiments demonstrate the effectiveness of the proposed semi-supervised ML model with the introduced safety indicators serving as important features. The proposed semi-supervised ML method outperforms other baseline semi-supervised or unsupervised methods regarding various metrics, e.g., delivering the best accuracy (99.58%) and the best F-1 measure (0.9913). The ablation study further highlights the significance of safety indicators for advancing the detection performance.

The ‘Dataset’ directory contains two datasets of indoor and outdoor PM2.5 data that were previously collected from field studies conducted by our research group at the Lawrence Berkeley National Laboratory. Dataset 1 contains PM2.5 data that were collected by Noris et al. (2013) from two-weeks of monitoring in 18 low-income apartments in California. Dataset 1 is used as the training dataset, where the indoor PM emission events were previously analyzed by Chan et al. (2018) using a rule-based method. Dataset 2 contains PM2.5 data that were collected by Singer et al. (2020) from 65 new California single-family homes for one week each.

The 18 apartments in Dataset 1 were identified by building number (‘Bldg’ = 1, 2, or 3), apartment number (‘Apt’ = 1 to 6), and whether the data was collected before (‘Period = 1) or after (‘Period = 2’) retrofit. The 65 single-family homes in Dataset 2 were identified by building number (‘Bldg’). An adjustment factor of 1.23 was applied to the indoor PM2.5...

The DOE and Berkeley Lab have partnered across the national laboratory complex and with the research community to curate, validate, and publish the world’s largest set of labeled time-series data representing commercial HVAC systems operating in faulted and fault-free states. The data sets currently cover rooftop units, single-duct air handler units, dual-duct air handler units, variable air volume boxes, fan coil units, chiller plant, and boiler plant. Each data set includes from 20 to more than 100 data points that are commonly monitored in today’s buildings. This operational data is paired with ground truth information indicating which faults are present during which time periods.

Health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications. These efforts are focused on developing high temperature, wireless, low cost, and durable products. In an effort to address technical issues concerning health monitoring, this article considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk to detect the disk faults and assess its structural integrity. The experimental results composed at a range of rotational speeds from tests conducted at the NASA Glenn Research Center’s Rotordynamics Laboratory are evaluated and integrated into multiple data-driven anomaly detection techniques to identify faults and anomalies in the disk. In summary, this study presents a select evaluation of online health monitoring of a rotating disk using high caliber capacitive sensors and demonstrates the capability of the in-house spin system.

The Laboratory Data Management and Analysis Software market is experiencing robust growth, driven by increasing adoption of LIMS (Laboratory Information Management Systems) and ELN (Electronic Lab Notebook) solutions across various research and development sectors. The market's expansion is fueled by the need for efficient data management, enhanced collaboration, regulatory compliance, and improved data analysis capabilities within laboratories. The rising volume of experimental data generated necessitates sophisticated software to streamline workflows, reduce errors, and accelerate research timelines. Pharmaceutical and biotechnology companies are major adopters, followed by academic institutions and contract research organizations. The trend towards cloud-based solutions and integration with other laboratory instruments is further driving market growth. Competitive pressures among software vendors are leading to innovative features, improved user interfaces, and cost-effective pricing models. While initial investment costs can be a barrier to entry for some smaller labs, the long-term benefits in terms of efficiency gains and reduced operational costs outweigh these considerations. The market is expected to maintain a strong growth trajectory, with continuous technological advancements and increasing demand from diverse sectors contributing to its expansion. The market's segmentation reveals a significant presence of established players like Agilent Technologies and Thermo Fisher Scientific, alongside emerging companies offering specialized solutions and catering to niche market segments. Strategic partnerships and acquisitions are common occurrences as larger players aim to expand their product portfolios and market reach. Geographic variations in market growth are influenced by factors such as technological infrastructure, regulatory landscapes, and research funding. North America and Europe currently dominate the market, but Asia-Pacific is anticipated to demonstrate substantial growth in the coming years, driven by increased investment in research and development across the region. Sustained innovation, alongside the ongoing need for efficient data management and advanced analytics within laboratories, will continue to shape the future trajectory of this dynamic market. We project continued strong growth, albeit at a moderating pace, as the market matures.

The data discovery market is experiencing robust growth, fueled by the increasing volume and complexity of data generated across various industries. The market, currently valued in the billions (a precise figure cannot be provided without the missing "XX" market size value, but a reasonable estimate based on similar market reports and a 21% CAGR would place it in the several billion-dollar range in 2025), is projected to maintain a Compound Annual Growth Rate (CAGR) of 21% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the digital transformation initiatives across enterprises are leading to a surge in data generation, creating a critical need for efficient data exploration and analysis tools. Secondly, the rise of big data analytics and the growing demand for data-driven decision-making across sectors, including BFSI, telecom, retail, and manufacturing, are significantly bolstering market demand. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of data discovery platforms, making them more user-friendly and effective in extracting valuable insights. The market is segmented by component (software and services), enterprise size (SMEs and large enterprises), and industry vertical, with BFSI, telecommunications, and retail showing strong adoption. However, despite the impressive growth trajectory, certain restraints exist. The high initial investment costs associated with implementing data discovery solutions can pose a challenge for smaller organizations. Additionally, the complexity of data integration and the need for skilled professionals to manage and interpret the results can hinder wider adoption. Nonetheless, the ongoing technological advancements and the increasing awareness of the strategic value of data are expected to mitigate these limitations, driving further market penetration. The competitive landscape includes both established players like SAS Institute and Salesforce (via Tableau), and emerging innovative companies, signifying a dynamic and evolving market with ample opportunities for growth and innovation. The geographical distribution of the market is likely to be skewed towards mature markets like North America and Europe initially, with Asia Pacific exhibiting strong growth potential in the coming years. Recent developments include: August 2022: CoreLogic, a major global provider of analytics-driven and property data solutions, expanded its partnership with Google Cloud to assist in the introduction of its novel CoreLogic Discovery Platform. Discovery Platform, which is fully built on Google Cloud's safe and sustainable technology, offers a complete asset analytics platform and cloud-based data interchange for enterprises in a variety of industries., June 2022: Select Star established an official collaboration with dbt Labs. Dbt has been one of Select Star's most significant integrations, with over 15,000 models and 225,000 columns linked up to date. Select Star is intended to facilitate the data discovery required by companies in order to harness the potential of their data and generate effective outcomes. As a result, Select Star and Dbt Labs have a shared goal, to empower analytics engineers to convert information better and keep appropriate documentation so that business users and data analysts can trust their data., June 2022: TD SYNNEX's SNX Tech Data established a collaboration with Instructure INST, a Learning Management Systems ("LMS") company, to utilize advanced learning capabilities in India. TD SYNNEX earned a substantial advantage with this deal, in addition to developing its data, Internet of Things, and analytics products. By enabling end-to-end business analytics powered by self-service data discovery, corporate reporting, mobile apps, and embedded analytics, TD SYNNEX's partners were able to offer complete business analytics propelled by data-driven business culture.. Key drivers for this market are: Increasing Number of Multi-Structured Data Sources, Growing Importance for Data-Driven Decision-Making. Potential restraints include: Data Security and Privacy Concerns. Notable trends are: The Banking, Financial Services, and Insurance Sector Holds a Dominant Position.

The data discovery market, projected at $XX million in 2025, is experiencing robust growth, fueled by a compound annual growth rate (CAGR) of 21%. This expansion is driven by several key factors. The increasing volume and complexity of data generated by businesses across diverse sectors necessitate efficient tools for data analysis and insights extraction. The rise of big data analytics and the growing adoption of cloud-based solutions are further propelling market growth. Businesses across industries, particularly Banking, Financial Services, and Insurance (BFSI), Telecommunications and IT, and Retail and E-commerce, are increasingly recognizing the value of data-driven decision-making, leading to higher adoption rates of data discovery platforms. The market is segmented by component (software and services), enterprise size (SMEs and large enterprises), and industry vertical, with each segment contributing uniquely to overall market dynamics. While the market faces challenges such as the need for skilled professionals and the complexity of integrating data from disparate sources, the overall trend suggests sustained growth, driven by the continuous rise in data generation and the expanding need for actionable insights. The competitive landscape is characterized by a mix of established players like Tableau, SAP, and Oracle, and emerging innovative companies. This competition fosters innovation and drives down costs, making data discovery solutions more accessible to a broader range of businesses. While North America currently holds a significant market share, regions like Asia Pacific are expected to witness rapid growth driven by increasing digitalization and adoption of advanced analytics. The forecast period (2025-2033) anticipates sustained growth, though the rate of expansion may gradually moderate as the market matures. The market's future trajectory will depend on factors such as technological advancements, regulatory changes, and the overall economic climate. Continued investment in research and development, coupled with strategic partnerships and acquisitions, will be key to success in this dynamic and rapidly evolving market. Recent developments include: August 2022: CoreLogic, a major global provider of analytics-driven and property data solutions, expanded its partnership with Google Cloud to assist in the introduction of its novel CoreLogic Discovery Platform. Discovery Platform, which is fully built on Google Cloud's safe and sustainable technology, offers a complete asset analytics platform and cloud-based data interchange for enterprises in a variety of industries., June 2022: Select Star established an official collaboration with dbt Labs. Dbt has been one of Select Star's most significant integrations, with over 15,000 models and 225,000 columns linked up to date. Select Star is intended to facilitate the data discovery required by companies in order to harness the potential of their data and generate effective outcomes. As a result, Select Star and Dbt Labs have a shared goal, to empower analytics engineers to convert information better and keep appropriate documentation so that business users and data analysts can trust their data., June 2022: TD SYNNEX's SNX Tech Data established a collaboration with Instructure INST, a Learning Management Systems ("LMS") company, to utilize advanced learning capabilities in India. TD SYNNEX earned a substantial advantage with this deal, in addition to developing its data, Internet of Things, and analytics products. By enabling end-to-end business analytics powered by self-service data discovery, corporate reporting, mobile apps, and embedded analytics, TD SYNNEX's partners were able to offer complete business analytics propelled by data-driven business culture.. Key drivers for this market are: Increasing Number of Multi-Structured Data Sources, Growing Importance for Data-Driven Decision-Making. Potential restraints include: Increasing Number of Multi-Structured Data Sources, Growing Importance for Data-Driven Decision-Making. Notable trends are: The Banking, Financial Services, and Insurance Sector Holds a Dominant Position.

The Laboratory Data Automation market is experiencing robust growth, projected to reach $210.9 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 10.7% from 2025 to 2033. This expansion is driven by several key factors. Increasing demand for improved laboratory efficiency and productivity is a major catalyst, as automation streamlines workflows, reduces human error, and accelerates research and development processes. The rising adoption of cloud-based LIMS (Laboratory Information Management Systems) and other data management solutions enhances data accessibility, collaboration, and regulatory compliance, further fueling market growth. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are integrating into laboratory data automation systems, enabling predictive analytics, automated data analysis, and enhanced decision-making capabilities. The pharmaceutical and biotechnology industries are significant contributors to this growth, driven by the need for efficient drug discovery and development. Competition among key players like Dotmatics (BioBright), EISC, Thermo Fisher Scientific, Benchling, Labforward GmbH, XiTechniX, and LabWare LIMS is fostering innovation and driving down costs, making these solutions more accessible to a wider range of laboratories. Growth in the coming years will be influenced by several factors. Continued advancements in technology, particularly in AI and machine learning applications within LIMS and other laboratory data automation tools, will be a primary driver. Increased regulatory scrutiny and the need for enhanced data security and compliance will also stimulate market growth. However, challenges such as the high initial investment costs associated with implementing these systems and the need for skilled personnel to operate and maintain them could act as potential restraints. Despite these challenges, the overall market outlook for laboratory data automation remains highly positive, driven by the compelling benefits of increased efficiency, improved data management, and accelerated research capabilities. The market's segmentation (though not specified in the prompt) will likely include solutions for specific laboratory types (e.g., clinical, research, industrial) and application areas (e.g., sample management, instrument control, data analysis).

This dataset and the content below if from this journal publication and Github repository.

Data science for building energy efficiency: A comprehensive text-mining review of scientific literature

This repository contains the data and code for paper:

Mahmoud M. Abdelrahman, Sicheng Zhan, Clayton Miller, and Adrian Chong (2021) Data science for building energy efficiency: A comprehensive data-driven review of scientific literature. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2021.110885.

@article{ABDELRAHMAN2021110885,
 title = {Data science for building energy efficiency: A comprehensive text-mining driven review of scientific literature},
 author = {Mahmoud M. Abdelrahman and Sicheng Zhan and Clayton Miller and Adrian Chong},
 journal = {Energy and Buildings},
 pages = {110885},
 year = {2021},
 issn = {0378-7788},
 doi = {https://doi.org/10.1016/j.enbuild.2021.110885}
}

Graphical abstract

https://user-images.githubusercontent.com/6969514/102309569-066e2400-3fa4-11eb-920d-381f177f44b4.jpg" alt="visual_abstractAsset 18@4x-80">

Licenses

Text and figures : CC-BY-4.0

Data set for "A data-driven approach coupled with physical constraints to improve groundwater models with structural error" includes three parts: (1) data used for the two case studies, a laboratory sand column anthracene transport case, e.g., the ratio of fluoranthene transport concentration to its initial concentration; a synthetic three-dimensional groundwater contaminant transport case, e.g., the contaminant concentraion in Well 1 and Well 2; (2) data used for plotting the Figures, e.g., Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, and Figure 8; (3) codes for DDMs without/with physical constraints.

The Laboratory Data Management System (LDMS) market is experiencing robust growth, driven by the increasing volume of laboratory data, the need for improved efficiency and accuracy in laboratory operations, and the rising adoption of electronic health records (EHRs). The market's expansion is further fueled by technological advancements such as cloud-based solutions, artificial intelligence (AI), and machine learning (ML) integration, enhancing data analysis and workflow automation. This allows labs to reduce manual errors, improve turnaround times, and ultimately, deliver better patient care. Consolidation within the industry through mergers and acquisitions is also shaping the competitive landscape, leading to the emergence of larger, more comprehensive LDMS providers. Furthermore, regulatory mandates emphasizing data security and interoperability are pushing labs to adopt advanced LDMS solutions to ensure compliance. We estimate the market size to be approximately $2.5 billion in 2025, growing at a Compound Annual Growth Rate (CAGR) of 7% over the forecast period (2025-2033). The key players in this market, including Clinisys, Veradigm, Oracle Corporation, and others, are constantly innovating to meet the evolving needs of laboratories. This includes developing solutions that support diverse laboratory workflows, integrating with other healthcare IT systems, and providing robust data security features. Challenges remain, however, primarily related to the high initial investment costs associated with implementing LDMS and the need for extensive training and support for lab personnel. Despite these challenges, the long-term prospects for the LDMS market remain positive, fueled by ongoing technological advancements, increasing regulatory pressures, and the persistent demand for improved laboratory efficiency and data management capabilities. The continued integration of AI and ML will be a crucial driver of future innovation and market growth within the sector.

Vehicle Detection Data Lab

## Overview

Vehicle Detection Data Lab is a dataset for object detection tasks - it contains Vehicles Pr4b annotations for 2,150 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The size of the Lab Information System Market was valued at USD 3.49 Million in 2023 and is projected to reach USD 8.10 Million by 2032, with an expected CAGR of 12.79% during the forecast period. The lab information system (LIS) market refers to the entire market of software solutions that handle, track, and preserve data relevant to laboratory operations, primarily in healthcare settings. The key objective of LIS is to facilitate streamlined laboratory processes, ensure accuracy in test results, regulatory compliance, and overall efficiency in workflows. These systems are important to clinical, diagnostic, research, and pharmaceutical labs to handle patient information, specimen tracking, test results, and inventory, also promoting reporting and billing. Rising demand for LIS is obliged to the extensive requirement for automated management in the laboratory fields by reducing human mistakes, faster turnaround times, and efficient use of available resources. With digitization and data-driven healthcare globally, the implementation of LIS is growing in regions such as North America, Europe, and Asia-Pacific. Trends in personalized medicine are also driving the market because laboratories require advanced systems for processing larger volumes of data, including genetic testing results and biomarkers. Regulatory pressures such as those from the FDA and other healthcare authorities have made it even more necessary for labs to have strong, compliant LIS systems in place to meet industry standards and protect the patient. Scalable cloud-based LIS solutions, enabling real-time data access, integration with other health IT systems, EHR and HMS, among others, are further increasing market demand. Recent developments include: In February 2022, Roche Diagnostics China entered China with Sanomede Medical Technology Co., Ltd. to jointly launch RS600 Lab Automation Software (LAS) for the Chinese Market., In February 2022, Biosero, Inc., a BICO company, and a developer of laboratory automation solutions to orchestrate scientific discoveries, launched several new products and features in the company's Green Button Go software suite. Together, these products deliver an array of advanced capabilities to help customers streamline, control, and generate better results from their automated lab systems.. Key drivers for this market are: Rising Demand for Bio-banking, Increasing Focus on Improvisation of Laboratory; Technological Advancements in LIMS Offerings. Potential restraints include: Cost Associated with Implementation of Laboratory Information Management System, Rising Data Security and Privacy Concerns. Notable trends are: Cloud-based Segment is Expected to Hold a Major Market Share in the Laboratory Information System Market.

These data were produced for ARO W911NF-14-1-0573 "Morphologically Modulated Dynamics" and ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems" to explore the trade-offs between various oscillator coupling models in modeling multilegged locomotion. The data were also used extensively in examining multi-contact slipping, in the studying the influence of number of legs on otherwise identical locomotion patterns, and in the use of geometric mechanics models for multilegged locomotion. Folder and file names encode the meta-data, with names following an informative naming convention documented in the README.

Static liquefaction landslides are particularly prevalent in loose saturated granular soils. Generally, they are catastrophic geological processes due to their high-speed and long-runout mobility. However, here can be challenging to accurately predict the initiation and failure of the static liquefaction landslides. Here, I compiled a database of undrained triaxial compression tests of 152 saturated loess at 15 sampling sites in the Chinese Loess Plateau. Meanwhile, I collected the landslide monitoring data from six flume tests and three field landslides. These data help to build and verify the laboratory data-driven sliding-block model. The model can serve as an effective tool for static liquefaction landslide prediction.

We tested the sensitivity of a portable, field-based environmental DNA (eDNA) platform relative to widely used lab-based eDNA approaches for detecting invasive northern pike (Esox lucius) in eight lakes on Alaska’s Kenai Peninsula. Raw data reported in this dataset report detect/non-detect data for technical replicates of water samples.

The Scientific Data Management Software (SDMS) market is experiencing robust growth, driven by the increasing volume and complexity of scientific data generated across various research domains. The market, estimated at $2.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This growth is fueled by several key factors. The rising adoption of cloud-based SDMS solutions offers enhanced scalability, accessibility, and collaboration capabilities, attracting researchers in academia and industry alike. Furthermore, stringent regulatory compliance requirements in sectors like pharmaceuticals and biotechnology are compelling organizations to invest in robust SDMS solutions for efficient data management and audit trails. The trend towards data-driven discovery and the integration of artificial intelligence (AI) and machine learning (ML) in scientific workflows further accelerates the market's expansion. Different application segments, including biological and life sciences, chemistry and material science, and environmental science, contribute significantly to the market's overall growth. The competitive landscape is marked by a blend of established players offering comprehensive solutions and emerging startups focusing on niche applications or innovative features. While the initial investment in SDMS implementation can be a barrier for some organizations, the long-term benefits in terms of improved efficiency, reduced costs, and enhanced data security outweigh the upfront expenses. The North American market currently holds a dominant share, attributed to the high concentration of research institutions, pharmaceutical companies, and biotechnology firms in the region. However, significant growth is anticipated in the Asia-Pacific region, driven by increasing government investments in research and development, coupled with the growing adoption of advanced technologies across various industries. The on-premises deployment model remains prevalent, particularly in organizations with stringent data security concerns. However, the cloud-based segment is poised for rapid expansion, driven by its flexibility, cost-effectiveness, and accessibility. Segmentation by application area (Biological and Life Sciences, Chemistry and Material Science, Environmental Science, and Others) and deployment type (Cloud-Based and On- Premises) provides a granular understanding of the market's dynamics. Continued innovation in areas such as data visualization, integration with laboratory instruments, and advanced analytics will be crucial for vendors to maintain a competitive edge in this dynamic market.