Synthetic dataset of nuclei on a tube-like tissue that changes shape, for analysis demonstration with TubULAR.

TubULAR is a set of tools for working with 3D data of surfaces – potentially complex and dynamic – that can be described as tubes. Developing guts, pumping hearts, and other visceral organs can be treated as tubes with potentially complex and dynamic shapes. With TubULAR, we can describe the tissue motion on the tube-like surface and quantify how it changes over time.

This synthetic dataset is a tube of cells with nuclei and membrane that coils into a loop, then uncoils into a straight tube. To generate the dataset, the surface geometry was encoded numerically. We placed 120 nuclei-like blobs of intensity centered at locations across the surface. Locations were chosen as a solution to an iterative farthest-point search, so that nuclei are well-spaced from each other. We then performed a Voronoi tessellation to create a channel mimicking `cell-cell junctions'. The nuclei sizes were determined based on the distance of each nucleus to the nearest membrane location.

For more on the codebase, visit: https://npmitchell.github.io/tubular/ https://github.com/npmitchell/tubular

This dataset contains 10,000 synthetic coronal loop videos generated for machine learning and data analysis applications in solar physics. Half of the samples (50%) include simulated kink oscillations of coronal loops, while the other half contain stationary loops without oscillations. Dataset structure: 1. kink_lab.zip – Metadata and parameters for the oscillating loop samples. 2. kink_movie.zip – Synthetic videos of coronal loops with kink oscillations. 3. stationary_lab.zip – Metadata and parameters for stationary loop samples. 4. stationary_movie.zip – Synthetic videos of stationary coronal loops (no oscillations).

This is an sqlite-lite binary file containing room temperature magnetic hysteresis loops and minor loops for a set of first order reversal curves (FORC)s. The data is generated using MERRILL (https://www.rockmag.org). It can be viewed using the command line sqlite (see https://www.sqlite.org). The file contains a single table, called 'all_loops' with the following fields:

id - a unique integer id for each row in the

geometry - a unique name of a geometry in this case 'oblate' and 'prolate' indicating an oblate truncated tetrahedron and a prolate truncated tetrahedron.

temperature - the temperature at which micromagnetic models were run in order to generate the data.

aspect_ratio - the aspect ratio of the geometries.

size - the size of each geometry - in nanometre, using a convention of equivalend spherical volume diameter.

Br - the reversal field of a minor loop.

B - the applied applied field.

M - the magnetisation that results from applying B (starting from Br).

SatMag - the saturation magnetisation value of magnetite at the given temperature.

In order for the file to be consistent, it is recommended that each field step B is uniform across all geometries, sizes and aspect ratios.

Snapshot img { margin: 10px !important; } Generative AI In Data Labeling Solution And Services Market Size 2025-2029

The generative ai in data labeling solution and services market size is forecast to increase by USD 31.7 billion, at a CAGR of 24.2% between 2024 and 2029.

The global generative AI in data labeling solution and services market is shaped by the escalating demand for high-quality, large-scale datasets. Traditional manual data labeling methods create a significant bottleneck in the ai development lifecycle, which is addressed by the proliferation of synthetic data generation for robust model training. This strategic shift allows organizations to create limitless volumes of perfectly labeled data on demand, covering a comprehensive spectrum of scenarios. This capability is particularly transformative for generative ai in automotive applications and in the development of data labeling and annotation tools, enabling more resilient and accurate systems.However, a paramount challenge confronting the market is ensuring accuracy, quality control, and mitigation of inherent model bias. Generative models can produce plausible but incorrect labels, a phenomenon known as hallucination, which can introduce systemic errors into training datasets. This makes ai in data quality a critical concern, necessitating robust human-in-the-loop verification processes to maintain the integrity of generative ai in healthcare data. The market's long-term viability depends on developing sophisticated frameworks for bias detection and creating reliable generative artificial intelligence (AI) that can be trusted for foundational tasks.

What will be the Size of the Generative AI In Data Labeling Solution And Services Market during the forecast period?

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The global generative AI in data labeling solution and services market is witnessing a transformation driven by advancements in generative adversarial networks and diffusion models. These techniques are central to synthetic data generation, augmenting AI model training data and redefining the machine learning pipeline. This evolution supports a move toward more sophisticated data-centric AI workflows, which integrate automated data labeling with human-in-the-loop annotation for enhanced accuracy. The scope of application is broadening from simple text-based data annotation to complex image-based data annotation and audio-based data annotation, creating a demand for robust multimodal data labeling capabilities. This shift across the AI development lifecycle is significant, with projections indicating a 35% rise in the use of AI-assisted labeling for specialized computer vision systems.Building upon this foundation, the focus intensifies on annotation quality control and AI-powered quality assurance within modern data annotation platforms. Methods like zero-shot learning and few-shot learning are becoming more viable, reducing dependency on massive datasets. The process of foundation model fine-tuning is increasingly guided by reinforcement learning from human feedback, ensuring outputs align with specific operational needs. Key considerations such as model bias mitigation and data privacy compliance are being addressed through AI-assisted labeling and semi-supervised learning. This impacts diverse sectors, from medical imaging analysis and predictive maintenance models to securing network traffic patterns against cybersecurity threat signatures and improving autonomous vehicle sensors for robotics training simulation and smart city solutions.

How is this Generative AI In Data Labeling Solution And Services Market segmented?

The generative ai in data labeling solution and services market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029,for the following segments. End-userIT dataHealthcareRetailFinancial servicesOthersTypeSemi-supervisedAutomaticManualProductImage or video basedText basedAudio basedGeographyNorth AmericaUSCanadaMexicoAPACChinaIndiaSouth KoreaJapanAustraliaIndonesiaEuropeGermanyUKFranceItalyThe NetherlandsSpainSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSouth AfricaUAETurkeyRest of World (ROW)

By End-user Insights

The it data segment is estimated to witness significant growth during the forecast period.

In the IT data segment, generative AI is transforming the creation of training data for software development, cybersecurity, and network management. It addresses the need for realistic, non-sensitive data at scale by producing synthetic code, structured log files, and diverse threat signatures. This is crucial for training AI-powered developer tools and intrusion detection systems. With South America representing an 8.1% market opportunity, the demand for localized and specia

Macrocyclic peptides are the prevalent way to mimic interface helices for disruption of protein interactions, but current strategies to do this via synthetic C-cap mimics are underdeveloped and suboptimal. Bioinformatic studies described here were undertaken to better understand Schellman loops, the most common C-caps in proteins, to design superior synthetic mimics. An algorithm (Schellman Loop Finder) was developed, and data mining with this led to the discovery that these secondary structures are often stabilized by combinations of three hydrophobic side chains, most frequently from Leu, to form hydrophobic triangles. That insight facilitated design of synthetic mimics, bicyclic Schellman loop mimics (BSMs), where the hydrophobic triumvirate was replaced by 1,3,5-trimethylbenzene. We demonstrate that BSMs can be made quickly and efficiently, and are more rigid and helix-inducing than the best current C-cap mimics, which are rare and all monocycles.

Description

This dataset contains detailed time-series data from fuel cell stack performance tests conducted before and after the implementation of an AI-based optimization system. Each record corresponds to a test run capturing minute-by-minute readings of key process variables, such as voltage, temperature, hydrogen flow rate, and pressure.

The dataset was designed to support predictive maintenance, efficiency improvement, and early failure detection research in hydrogen fuel cell technology.

It can be used for:

Developing machine learning models to predict early failures.

Analyzing the impact of AI feedback loops on test efficiency and hydrogen usage.

Studying operational patterns of fuel cells under different testing regimes.

Dataset Structure Column Name Type Description test_id string Unique identifier for each fuel cell test (e.g., “Legacy_000”). test_type string Test category—either Before_AI or After_AI, indicating if the AI feedback loop was used. time_min integer Time elapsed (in minutes) since the test started. voltage_v float Measured stack voltage (in volts). temperature_c float Operating temperature of the stack (in °C). pressure_kpa float Stack chamber pressure (in kilopascals). h2_flow_lpm float Hydrogen flow rate (in liters per minute). outcome string Final test result—Pass or Fail. duration_min integer Total duration of the test (in minutes).

Key Insights The dataset includes 26,930 rows across 9 features.

Use Cases

Machine learning model training (classification/regression).

Time-series forecasting.

Process optimization studies.

Research on sustainable hydrogen testing.

License

CC BY 4.0 — You are free to use, share, and adapt the dataset with proper attribution.

Citation

If you use this dataset, please cite it as

Upadhyay, Shree (2025). Fuel Cell Stack Performance Testing Dataset (Before & After AI Optimization). Kaggle.

Developed by AI4Privacy, this dataset represents a pioneering effort in the realm of privacy and AI. As an expansive resource hosted on Hugging Face at ai4privacy/pii-masking-200k, it serves a crucial role in addressing the growing concerns around personal data security in AI applications.

Sources: The dataset is crafted using proprietary algorithms, ensuring the creation of synthetic data that avoids privacy violations. Its multilingual composition, including English, French, German, and Italian texts, reflects a diverse source base. The data is meticulously curated with human-in-the-loop validation, ensuring both relevance and quality.

Context: In an era where data privacy is paramount, this dataset is tailored to train AI models to identify and mask personally identifiable information (PII). It covers 54 PII classes and extends across 229 use cases in various domains like business, education, psychology, and legal fields, emphasizing its contextual richness and applicability.

Inspiration: The dataset draws inspiration from the need for enhanced privacy measures in AI interactions, particularly in LLMs and AI assistants. The creators, AI4Privacy, are dedicated to building tools that act as a 'global seatbelt' for AI, protecting individuals' personal data. This dataset is a testament to their commitment to advancing AI technology responsibly and ethically.

This comprehensive dataset is not just a tool but a step towards a future where AI and privacy coexist harmoniously, offering immense value to researchers, developers, and privacy advocates alike.

Context

This dataset takes the solar generation data from the Solar Power Generation Data dataset and uses it to simulate a realistic scenario: a solar panel array charging a battery that powers a constant load (e.g., a security camera system, a small server, or an IoT gateway). This synthetic dataset is highly interactive: you can create and simulate your own system with provided notebook.

The result is a rich dataset perfect for training and testing machine learning models that can tackle real-world energy management challenges, such as:

• Forecasting Battery State of Charge (SOC): How much energy will be in the battery in 6, 12, or 24 hours?
• Predicting Battery Depletion: When will the battery run out of energy?
• Optimizing Energy Use: Developing strategies to manage load based on predicted generation and battery state.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F25736304%2F06861c5310567f64304d7d2f0a14ccdb%2Fbattery_analysis.png?generation=1754055232258392&alt=media" alt="">

Content

This dataset contains a single CSV file, battery_dataset_with_features.csv (67.42 MB) . It was generated by combining Plant_x_Generation_Data.csv and Plant_x_Weather_Sensor_Data.csv from the source dataset and then running a simulation loop over the 34-day period. Simulation Parameters

The simulation was run with the following assumptions for a small-scale, off-grid system:

• Battery Capacity: 10 kWh
• Constant Load: 0.25 kW (250 Watts)
• Charging Efficiency: 90% (energy lost when charging the battery)
• Inverter Efficiency: 95% (energy lost converting DC solar power to AC)
• Initial State of Charge: 50%

Column Descriptions

The dataset includes original columns from the source and new, simulated columns.

Original Columns:

• DATE_TIME: Timestamp for each record (at 1-minute intervals).
• PLANT_ID: Unique identifier for the solar plant (constant value of 2).
• SOURCE_KEY: Unique identifier for the inverter.
• DC_POWER: DC power generated by the solar panel array at that minute (in W).
• AC_POWER: AC power converted by the inverter at that minute (in W).
• AMBIENT_TEMPERATURE: The surrounding air temperature (°C).
• MODULE_TEMPERATURE: The temperature of the solar modules (°C).
• IRRADIATION: The amount of solar irradiance hitting the panels (W/m²).

New Simulated Columns:

• time_interval_hours: The calculated time between records in hours (approx. 1/60).
• battery_kwh: (Target Variable) The simulated amount of energy stored in the battery at the end of the minute (in kWh).
• battery_percentage: (Target Variable) The simulated battery State of Charge (SOC) as a percentage (0-100%).
• is_critical: A boolean flag (True if battery_percentage is below 20%). Useful for classification tasks.
• is_empty: A boolean flag (True if battery_percentage is at or below 1%). Useful for predicting critical failure events.

Acknowledgements

This dataset is derived from and builds upon the excellent "Solar Power Generation Data" dataset provided by Anik Ann. We extend our gratitude to the original author for making this valuable data publicly available.

Original Dataset Link: https://www.kaggle.com/datasets/anikannal/solar-power-generation-data

Inspiration

This dataset is a great playground for a variety of machine learning tasks. Here are a few ideas:

• Time Series Forecasting: Can you build a model (LSTM, GRU, Transformer, or ARIMA) to accurately predict battery_percentage 24 hours into the future?
• Classification: Can you build a classifier to predict if is_critical will be True within the next 6 hours? This is a crucial task for system reliability.
• Feature Engineering: What are the most important features for predicting the battery's state? Is it the recent DC_POWER, the MODULE_TEMPERATURE, or the IRRADIATION from the previous day?
• Anomaly Detection: Can you identify periods where the battery is discharging faster than expected, potentially indicating a system fault or an unexpected load?

This dataset provides comprehensive, structured information on provider referrals and care coordination, including referring and specialist provider details, patient information, referral reasons, authorization status, appointment scheduling, and closed-loop communication. It enables healthcare organizations to analyze referral patterns, optimize network utilization, and improve care continuity and outcomes.

The cyclopolymerization of α,ω-dienes catalyzed by transition metals (TMs) is one of the most attractive synthetic routes for the production of cyclic polyolefins (COPs). These COPs exhibit unique properties that confer enhanced performance and durability, making them highly desirable for advanced applications. By variation of the catalytic system, controlled microstructures of COPs can be achieved, particularly regarding the configuration of cyclic units and the cyclization ratio. The relationship between the catalyst structure, diastereoselectivity, and cyclization efficiency in the cyclopolymerization of 1,5-hexadiene (1,5-HD) and 1,7-octadiene (1,7-OD) has been explored by a combined study based on the density functional theory (DFT) calculation and experimental study involving the synthesis and characterization of the resulting polymers. DFT calculations explained the trans-selectivity of the majority of metallocene and nonmetallocene systems as well as the peculiar cis-selectivity of the nonmetallocene pyridylamido complex for 1,5-HD polymerization. The predicted diastereoselectivity was successfully corroborated by 1H and 13C NMR spectroscopic data collected from the synthesized polymers. Analyses by WAXS and DSC and the study of mechanical properties were performed to investigate their structural/property relationships. DFT calculations have been used also for explaining the experimental switching to cis-selectivity for the cyclopolymerization of 1,7-OD achieved by the TM systems promoting the trans-selectivity of 1,5-HD. The comparison with the enantioselectivity of α-olefin polymerization has been used as a key guideline for this work closing the loop between the diastereoselectivity of nonconjugated α,ω-diolefin cyclopolymerization and the enantioselectivity of the α-olefin polymerization.

High-resolution temporal data on contacts between hosts provide crucial information on the mixing patterns underlying infectious disease transmission. Publicly available data sets of contact data are however typically recorded over short time windows with respect to the duration of an epidemic. To inform models of disease transmission, data are thus often repeated several times, yielding synthetic data covering long enough timescales. Looping over short term data to approximate contact patterns on longer timescales can lead to unrealistic transmission chains because of the deterministic repetition of all contacts, without any renewal of the contact partners of each individual between successive periods. Real contacts indeed include a combination of regularly repeated contacts (e.g., due to friendship relations) and of more casual ones. In this paper, we propose an algorithm to longitudinally extend contact data recorded in a school setting, taking into account this dual aspect of contacts and in particular the presence of repeated contacts due to friendships. To illustrate the interest of such an algorithm, we then simulate the spread of SARS-CoV-2 on our synthetic contacts using an agent-based model specific to the school setting. We compare the results with simulations performed on synthetic data extended with simpler algorithms to determine the impact of preserving friendships in the data extension method. Notably, the preservation of friendships does not strongly affect transmission routes between classes in the school but leads to different infection pathways between individual students. Our results moreover indicate that gathering contact data during two days in a population is sufficient to generate realistic synthetic contact sequences between individuals in that population on longer timescales. The proposed tool will allow modellers to leverage existing contact data, and contributes to the design of optimal future field data collection.

Snapshot img { margin: 10px !important; } Generative AI In Asset Management Market Size 2025-2029

The generative ai in asset management market size is forecast to increase by USD 689.7 million, at a CAGR of 23.3% between 2024 and 2029.

The imperative for operational efficiency is a primary driver in the global generative AI in asset management market. By enabling investment research automation and back-office process automation, the technology allows highly skilled professionals to shift their focus from routine data processing to strategic decision-making and client relationship building. This application of generative ai in banking and finance is critical for maintaining competitiveness. The ongoing trend involves a strategic shift from general-purpose AI to domain-specific language models. These models, purpose-built for the intricacies of finance, provide a more reliable foundation for contextual financial analysis and other high-stakes applications within artificial intelligence (AI) in asset management, addressing some of the limitations of broader systems.A formidable challenge impeding wider adoption is the issue of AI model hallucinations and the need for factual inaccuracy mitigation. The generation of plausible but incorrect information presents significant risks in an environment where decisions are based on precise data, necessitating stringent human-in-the-loop validation processes. This reliability issue underscores the critical need for robust AI model governance and continuous performance monitoring to ensure the integrity of AI-generated insights. These dynamics shape the landscape for generative ai in data analytics, pushing the industry toward a model where technology augments human expertise rather than fully replacing it, ensuring both efficiency gains and responsible implementation of generative ai in trading.

What will be the Size of the Generative AI In Asset Management Market during the forecast period?

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The global generative ai in asset management market is transforming, leveraging large language models and natural language processing for applications from investment thesis generation to client communication automation. This evolution enhances portfolio optimization algorithms and enables ai-driven client personalization. The technology extends to automated report generation and financial document summarization, facilitating investment research automation. Moreover, capabilities in financial sentiment analysis and earnings call analysis refine investment decision support systems. This shift is critical for developing algorithmic trading strategies and improving market trend prediction for alpha signal generation. The integration of an ai copilot assistant further augments knowledge management automation, changing the operational fabric from reactive analysis to proactive strategy formulation.The increasing adoption of proprietary ai applications necessitates robust frameworks for risk mitigation. Central to this is ai model governance and model risk management to address ai model hallucinations and ensure algorithmic bias mitigation. Demand for transparency is driving the adoption of explainable ai, often with human-in-the-loop validation. As firms navigate deployment between on-premises ai deployment and a hybrid cloud ai architecture, data privacy in ai and ai ethics in finance are paramount. Concurrently, regulatory compliance automation, ai-powered compliance monitoring, and ai-driven fraud detection are becoming indispensable for back-office process automation. Projections indicate this focus on responsible deployment could influence over 35% of new technology investments, balancing innovation with risk modeling and simulation using synthetic data generation for comprehensive stress testing scenarios.

How is this Generative AI In Asset Management Market segmented?

The generative ai in asset management market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029,for the following segments. DeploymentCloud basedOn premisesHybridApplicationPortfolio managementRisk managementClient engagement and personalizationResearch and analysisOthersEnd-userAsset management firmsBanks and financial institutionsInsurance companiesCorporate firmsGeographyNorth AmericaUSCanadaMexicoAPACJapanChinaIndiaSouth KoreaAustraliaIndonesiaEuropeUKGermanyFranceItalyThe NetherlandsSpainSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSouth AfricaUAETurkeyRest of World (ROW)

By Deployment Insights

The cloud based segment is estimated to witness significant growth during the forecast period.

The cloud-based model is the dominant and most rapidly expanding deployment method. Its prominence is driven by significant advantages, i

This dataset comprises all the data resulting from the model of our dynamic pathway regulation strategy for cell factories: The Extended metabolic TF-based biosensor antithetic controller (EMBA). Analysis of this dataset was performed and published in the article “Extended metabolic biosensor design for dynamic pathway regulation of cell factories” Yadira Boadaa, Alejandro Vignoni, Jesús Picó, Pablo Carbonell, 2020, iScience. DOI: https://doi.org/10.1016/j.isci.2020.101305 Different data in the dataset was obtained by changing some of the simulation parameters.

Perturbation Data includes: -Time-courses of Naringenin and CHS for the open-loop and the closed-loop for varying available amounts of Malonyl-CoA (in number of molecules per cell) -Static (equilibrium) production level of Naringenin for the open-loop and the closed-loop for varying available amounts of Malonyl-CoA (in g/L).

Robustness Data includes: -Static (equilibrium) production level of Naringenin (in g/L). for the direct controller and the antithetic controller for varying available amounts of Malonyl-CoA (100% and 60% of available malonyl-CoA) for each one of the parameter combinations. -Static levels of Naringenin vs CHS, Naringenin Vs Kaempferol vs anti-sigma and Anti-sigma vs CHS; for 15% CV random values of the pathway enzymes to obtain the plots of the different Transfer Function (dose-response curve) circuit parts (metabolic part of the biosensor, TF-based biosensor and controller, and overall sensor-controller transfer function).

Mobile Robot Data Annotation Tools Market Outlook

According to our latest research, the global mobile robot data annotation tools market size reached USD 1.46 billion in 2024, demonstrating robust expansion with a compound annual growth rate (CAGR) of 22.8% from 2025 to 2033. The market is forecasted to attain USD 11.36 billion by 2033, driven by the surging adoption of artificial intelligence (AI) and machine learning (ML) in robotics, the escalating demand for autonomous mobile robots across industries, and the increasing sophistication of annotation tools tailored for complex, multimodal datasets.

The primary growth driver for the mobile robot data annotation tools market is the exponential rise in the deployment of autonomous mobile robots (AMRs) across various sectors, including manufacturing, logistics, healthcare, and agriculture. As organizations strive to automate repetitive and hazardous tasks, the need for precise and high-quality annotated datasets has become paramount. Mobile robots rely on annotated data for training algorithms that enable them to perceive their environment, make real-time decisions, and interact safely with humans and objects. The proliferation of sensors, cameras, and advanced robotics hardware has further increased the volume and complexity of raw data, necessitating sophisticated annotation tools capable of handling image, video, sensor, and text data streams efficiently. This trend is driving vendors to innovate and integrate AI-powered features such as auto-labeling, quality assurance, and workflow automation, thereby boosting the overall market growth.

Another significant growth factor is the integration of cloud-based data annotation platforms, which offer scalability, collaboration, and accessibility advantages over traditional on-premises solutions. Cloud deployment enables distributed teams to annotate large datasets in real time, leverage shared resources, and accelerate project timelines. This is particularly crucial for global enterprises and research institutions working on cutting-edge robotics applications that require rapid iteration and continuous learning. Moreover, the rise of edge computing and the Internet of Things (IoT) has created new opportunities for real-time data annotation and validation at the source, further enhancing the value proposition of advanced annotation tools. As organizations increasingly recognize the strategic importance of high-quality annotated data for achieving competitive differentiation, investment in robust annotation platforms is expected to surge.

The mobile robot data annotation tools market is also benefiting from the growing emphasis on safety, compliance, and ethical AI. Regulatory bodies and industry standards are mandating rigorous validation and documentation of AI models used in safety-critical applications such as autonomous vehicles, medical robots, and defense systems. This has led to a heightened demand for annotation tools that offer audit trails, version control, and compliance features, ensuring transparency and traceability throughout the model development lifecycle. Furthermore, the emergence of synthetic data generation, active learning, and human-in-the-loop annotation workflows is enabling organizations to overcome data scarcity challenges and improve annotation efficiency. These advancements are expected to propel the market forward, as stakeholders seek to balance speed, accuracy, and regulatory requirements in their AI-driven robotics initiatives.

From a regional perspective, Asia Pacific is emerging as a dominant force in the mobile robot data annotation tools market, fueled by rapid industrialization, significant investments in robotics research, and the presence of leading technology hubs in countries such as China, Japan, and South Korea. North America continues to maintain a strong foothold, driven by early adoption of AI and robotics technologies, a robust ecosystem of annotation tool providers, and supportive government initiatives. Europe is also witnessing steady growth, particularly in the manufacturing and automotive sectors, while Latin America and the Middle East & Africa are gradually catching up as awareness and adoption rates increase. The interplay of regional dynamics, regulatory environments, and industry verticals will continue to shape the competitive landscape and growth trajectory of the global market over the forecast period.

• ./data/train.tsv and ./data/dev.tsv are TSV files consisting of topics (queries), facets, clarifying questions, user's answers, and labels for how much clarification is needed (clarification needs).
• ./data/test.tsv is a TSV file consisting of test topic ID's, as well as queries (text).
• ./data/test_with_labels.tsv is a TSV file consiting of test topic ID's with the labels. It can be used with the evaluation script.
• ./data/multi_turn_human_generated_data.tsv is a TSV file containing the human-generated multi turn conversations which is the result of of the human-in-the-loop process.
• ./data/question_bank.tsv is a TSV file containing all the questions in the collection, as well as their ID's. Participants' models should select questions from this file.
• ./data/top10k_docs_dict.pkl.tar.gz is a dict containing the top 10,000 document ID's retrieved from ClueWeb09 and ClueWeb12 collections for each topic. This may be used by the participants who wish to leverage documents content in their models.
• ./data/single_turn_train_eval.pkl is a dict containing the performance of each topic after asking a question and getting the answer. The evaluation tool that we provide uses this file to evaluate the selected questions.
• ./data/multi_turn_train_eval.pkl.tar.gz.** and ./data/multi_turn_dev_eval.pkl.tar.gz are dicts that contain the performance of each conversation after asking a question from the question_bank and getting the answer from the user. The evaluation tool that we provide uses this file to evaluate the selected questions. Notice that these dicts are built based on the synthetic multi-turn conversations.
• ./data/dev_synthetic.pkl.tar.gz and ./data/train_synthetic.pkl.tar.gz are two compressed pickle files that contain dicts of synthetic multi-turn conversations. We have generated these conversations following the method explained in [1].
• ./src/clariq_eval_tool.py is a python script to evaluate the runs. The participants may use this tool to evaluate their models on the dev set. We would use the same tool to evaluate the submitted runs on the test set.
• ./sample_runs/ contains some sample runs and baselines. Among them, we have included the two oracle models BestQuestion and WorstQuestion, as well as NoQuestion, the model choosing no question. Participants may check these files as sample run files. Also, they could test the evaluation tool using these files.

Staphylococcal enterotoxin B (SEB) exposure triggers an exaggerated pro-inflammatory cytokine response that often leads to toxic shock syndrome (TSS) associated with organ failure and death. MyD88 mediates pro-inflammatory cytokine signaling induced by SEB exposure and MyD88−/− mice are resistant to SEB intoxication, suggesting that MyD88 may be a potential target for therapeutic intervention. We targeted the BB loop region of the Toll/IL-1 receptor (TIR) domain of MyD88 to develop small-molecule therapeutics. Here, we report that a synthetic compound (EM-163), mimic to dimeric form of BB-loop of MyD88 attenuated tumor necrosis factor (TNF)- α, interferon (IFN)-γ, interleukin (IL)-1β, IL-2 and IL-6 production in human primary cells, whether administered pre- or post-SEB exposure. Results from a direct binding assay, and from MyD88 co-transfection/co-immunoprecipitation experiments, suggest that EM-163 inhibits TIR-TIR domain interaction. Additional results indicate that EM-163 prevents MyD88 from mediating downstream signaling. In an NF-kB-driven reporter assay of lipopolysaccharide-stimulated MyD88 signaling, EM-163 demonstrated a dose-dependent inhibition of reporter activity as well as TNF-α and IL-1β production. Importantly, administration of EM-163 pre- or post exposure to a lethal dose of SEB abrogated pro-inflammatory cytokine responses and protected mice from toxic shock-induced death. Taken together, our results suggest that EM-163 exhibits a potential for therapeutic use against SEB intoxication.

This data set serves as training and testing data for modelling the temperature field emanating from open loop groundwater heat pumps (100, randomly placed). It is simulated with Pflotran and saved in h5 format. It contains 101 data points, each consisting of one simulation run until a quasi-steady state is reached. Each data point measures 12.8 km x 12.8 km x 5 m with 2560 x 2560 x 1 cells. The varying parameters of the data sets are the positions of the heat pumps and a heterogeneous permeability field (Perlin noise, fixed min/max value). Other parameters that define the data sets, such as porosity and hydraulic pressure gradient are chosen to be as close as possible to reality. Source: "Die hydraulischen Grundwasserverhältnisse des quartären und des oberflächennahen tertiären Grundwasserleiters im Großraum München", Geologica Bavarica Volume 122.

IntroductionAge-related macular degeneration (AMD) is one of the leading causes of vision impairment globally and early detection is crucial to prevent vision loss. However, the screening of AMD is resource dependent and demands experienced healthcare providers. Recently, deep learning (DL) systems have shown the potential for effective detection of various eye diseases from retinal fundus images, but the development of such robust systems requires a large amount of datasets, which could be limited by prevalence of the disease and privacy of patient. As in the case of AMD, the advanced phenotype is often scarce for conducting DL analysis, which may be tackled via generating synthetic images using Generative Adversarial Networks (GANs). This study aims to develop GAN-synthesized fundus photos with AMD lesions, and to assess the realness of these images with an objective scale.MethodsTo build our GAN models, a total of 125,012 fundus photos were used from a real-world non-AMD phenotypical dataset. StyleGAN2 and human-in-the-loop (HITL) method were then applied to synthesize fundus images with AMD features. To objectively assess the quality of the synthesized images, we proposed a novel realness scale based on the frequency of the broken vessels observed in the fundus photos. Four residents conducted two rounds of gradings on 300 images to distinguish real from synthetic images, based on their subjective impression and the objective scale respectively.Results and discussionThe introduction of HITL training increased the percentage of synthetic images with AMD lesions, despite the limited number of AMD images in the initial training dataset. Qualitatively, the synthesized images have been proven to be robust in that our residents had limited ability to distinguish real from synthetic ones, as evidenced by an overall accuracy of 0.66 (95% CI: 0.61–0.66) and Cohen’s kappa of 0.320. For the non-referable AMD classes (no or early AMD), the accuracy was only 0.51. With the objective scale, the overall accuracy improved to 0.72. In conclusion, GAN models built with HITL training are capable of producing realistic-looking fundus images that could fool human experts, while our objective realness scale based on broken vessels can help identifying the synthetic fundus photos.

One goal of synthetic biology is to provide well-characterised biological parts that behave predictably in genetic assemblies. To achieve this, each part must be characterised in a time-resolved manner under relevant conditions. Robotic platforms can be used to automate this task and provide sufficiently large and reproducible data sets including provenance. Although robotics can significantly speed up the data collection process, the collation and analysis of the resulting data, needed to reprogram and refine workflows for future iterations, is often a manual process. As a result, even in times of rapidly advancing artificial intelligence, the common design-build-test-learn (DBTL) cycle is still not circular without human intervention. To move towards fully automated DBTL cycles, we developed a software framework to enable a robotic platform to autonomously adjust test parameters. This interdisciplinary work between computer science and biology thus transforms a static robotic platform into a dynamic one. The software framework includes software components such as an importer that retrieves measurement data from the platform’s devices and writes it to a database. This is followed by an optimizer that selects the next measurement points based on a balance between exploration and exploitation. The platform is shown to be able to automatically and autonomously optimize the inducer concentration for a Bacillus subtilis system and the combination of inducer and feed release for a Escherichia coli system. As a target product the readily measurable green fluorescent reporter protein (GFP) is produced over multiple, consecutive iterations of testing. An evaluation of chosen (learning) algorithms for single and dual factor optimization was performed. In this article, we share the lessons learned from the development, implementation and execution of this automated design-build-test-learn cycles on a robotic platform.

Additional file 1. DNA and protein sequences of construct, translated sequences and cloning syntaxes.