Establishment of a syndication flow from the department of Vendée (85) with the data from the “Restoration” schedule with enriched data: name, address, type of restaurant, means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, payment methods accepted, opening dates, rates, details of visits, videos.

Flow of syndication with data from the “sports and cultural activities”, “Leisure equipment”, “Cultural heritage”, “Natural heritage” and “Testations” with enriched data: name, address, type of activity/equipment, means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, payment methods accepted, opening dates, rates, details of visits, videos.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Establishment of a syndication flow from the department of Vendée (85) with data from the “Festivals and Events” schedule with enriched data: name, address, type of FMA (Fests and events), means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, methods of payment accepted, opening dates, rates, details of visits, videos.

This dataset has the long term mean (monthly climatology) of the 2-degree, Global, Enhanced, simple gridded monthly summary product (1950 - 1979) from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), the most extensive collection of surface marine data.

Success.ai’s Startup Data with Contact Data for Startup Founders Worldwide provides businesses with unparalleled access to key entrepreneurs and decision-makers shaping the global startup landscape. With data sourced from over 170 million verified professional profiles, this dataset offers essential contact details, including work emails and direct phone numbers, for founders in various industries and regions.

Whether you’re targeting tech innovators in Silicon Valley, fintech entrepreneurs in Europe, or e-commerce trailblazers in Asia, Success.ai ensures that your outreach efforts reach the right individuals at the right time.

Why Choose Success.ai’s Startup Founders Data?

1. Comprehensive Contact Information
2. Access verified work emails, phone numbers, and LinkedIn profiles for founders and key startup executives worldwide.

3. AI-driven validation ensures 99% accuracy, providing reliable data for effective outreach.

4. Global Reach Across Startup Ecosystems

5. Includes profiles of startup founders from tech, healthcare, fintech, sustainability, and other emerging sectors.

6. Covers North America, Europe, Asia-Pacific, South America, and the Middle East, helping you connect with founders on a global scale.

7. Continuously Updated Datasets

8. Real-time updates mean you always have the latest contact information, ensuring your outreach is timely and relevant.

9. Ethical and Compliant

10. Adheres to GDPR, CCPA, and global data privacy regulations, ensuring ethical and compliant use of data.

Data Highlights

• 170M+ Verified Professional Profiles: Includes startup founders and their teams across a range of industries.
• 50M Work Emails: AI-validated for precision in communication.
• 30M Company Profiles: Gain insights into the startups’ size, location, and industry focus.
• 700M Global Professional Profiles: Enriched data for comprehensive outreach and analysis.

Key Features of the Dataset:

1. Founder Decision-Maker Profiles
2. Identify and connect with founders and key executives who drive strategy, funding, and product decisions.

3. Engage with individuals who can approve partnerships, investments, and collaborations.

4. Advanced Filters for Precision Targeting

5. Filter by industry, funding stage, region, or startup size to narrow down your outreach efforts.

6. Ensure your campaigns target the most relevant contacts for your products, services, or investment opportunities.

7. AI-Driven Enrichment

8. Profiles are enriched with actionable data, offering insights that help tailor your messaging and improve response rates.

Strategic Use Cases:

1. Investor Relations and Funding Opportunities

2. Connect with founders seeking investment, pitch your venture capital or angel investment services, and establish long-term partnerships.

3. Business Development and Partnerships

4. Offer collaboration opportunities, strategic alliances, and joint ventures to startups in need of new market entries or product expansions.

5. Marketing and Sales Campaigns

6. Launch targeted email and phone outreach to founders who match your ideal customer profile, driving product adoption and long-term client relationships.

7. Recruitment and Talent Acquisition

8. Reach founders who may be open to recruitment partnerships or HR solutions, helping them build strong teams and scale effectively.

Why Choose Success.ai?

1. Best Price Guarantee

2. Enjoy top-quality, verified startup founder data at competitive prices, ensuring maximum return on investment.

3. Seamless Integration

4. Easily integrate verified contact data into your CRM or marketing platforms via APIs or customizable downloads.

5. Data Accuracy with AI Validation

6. With 99% data accuracy, you can trust the information to guide meaningful and productive outreach campaigns.

7. Customizable and Scalable Solutions

8. Tailor the dataset to your needs, focusing on specific industries, regions, or funding stages, and easily scale as your business grows.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enrich your existing CRM records with verified founder contact data, adding valuable insights for targeted engagements.

3. Lead Generation API

4. Automate lead generation and streamline your campaigns, ensuring efficient and scalable outreach to startup founders worldwide.

Leverage Success.ai’s B2B Contact Data for Startup Founders Worldwide to connect with the entrepreneurs driving innovation across global markets. With verified work emails, phone numbers, and continuously updated profiles, your outreach efforts become more impactful, timely, and effective.

Experience AI-validated accuracy and our Best Price Guarantee. Contact Success.ai today to learn how our B2B contact data solutions can help you engage with the startup founders who matter most.

No one beats us on price. Period.

DRAKO is a Mobile Location Data provider with a programmatic trading desk specializing in geolocation analytics and programmatic advertising. Our Tourism Data has helped cities, counties, and states better understand who their visitors are so that they can effectively develop and deliver advertising campaigns. We’re in a unique position to deliver enriched insight beyond traditional surveying or other data sources because of our rich dataset, proprietary modelling capabilities, and analytical capabilities.

MAIDs (Mobile Advertising IDs) are unique device identifiers associated with consenting mobile devices that can be utilized for geolocation based analyses and audiences. Drako uses MAIDs to fuel our Tourism Data utilizing our Home Location Model. The Home Location of a MAID is determined based on where that MAID is seen most frequently between the hours of 11pm and 6am (local time). Using this we are able to determine the Home Location of a user which in turn allows us to identify when and where they are travelling.

Beyond identifying that users are tourists, we can also classify them into different bins by their frequency / dwell time over their estimated number of visits. Using our data and frequency, we can identify: overnight visitors, weekend visits, short-term stays, long-term stays, or frequent holiday visitors !

Beyond Tourism Data in your defined geography alone, we are also able to provide: - Home location - Find out where your audience is coming from using our home location technology - Movement - Quantify how far users have travelled between locations. - Demographics - Discover neighborhood level characteristics such as income, ethnicity, and more - Brand index - Learn which major brands and retailers your audience is visiting the most. - Visitation index - See which destinations your visitors are visiting the most - Addressable audience - Customize your audiences for your campaigns using our analytic insights

Moreover, if you’re looking to activate your Tourism Data for advertising, we’re always able to further refine or filter your desired audience with our other Audience Data, such as: Brand visits, Geodemographics, Ticketed Event visits, Purchase Intent (in Canada), Purchase History (in USA), and more !

Data Compliance: All of our Tourism Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

Ethical Data ManagementExecutive SummaryIn the age of data and information, it is imperative that the City of Virginia Beach strategically utilize its data assets. Through expanding data access, improving quality, maintaining pace with advanced technologies, and strengthening capabilities, IT will ensure that the city remains at the forefront of digital transformation and innovation. The Data and Information Management team works under the purpose:“To promote a data-driven culture at all levels of the decision making process by supporting and enabling business capabilities with relevant and accurate information that can be accessed securely anytime, anywhere, and from any platform.”To fulfill this mission, IT will implement and utilize new and advanced technologies, enhanced data management and infrastructure, and will expand internal capabilities and regional collaboration.Introduction and JustificationThe Information technology (IT) department’s resources are integral features of the social, political and economic welfare of the City of Virginia Beach residents. In regard to local administration, the IT department makes it possible for the Data and Information Management Team to provide the general public with high-quality services, generate and disseminate knowledge, and facilitate growth through improved productivity.For the Data and Information Management Team, it is important to maximize the quality and security of the City’s data; to develop and apply the coherent management of information resources and management policies that aim to keep the general public constantly informed, protect their rights as subjects, improve the productivity, efficiency, effectiveness and public return of its projects and to promote responsible innovation. Furthermore, as technology evolves, it is important for public institutions to manage their information systems in such a way as to identify and minimize the security and privacy risks associated with the new capacities of those systems.The responsible and ethical use of data strategy is part of the City’s Master Technology Plan 2.0 (MTP), which establishes the roadmap designed by improve data and information accessibility, quality, and capabilities throughout the entire City. The strategy is being put into practice in the shape of a plan that involves various programs. Although these programs was specifically conceived as a conceptual framework for achieving a cultural change in terms of the public perception of data, it basically covers all the aspects of the MTP that concern data, and in particular the open-data and data-commons strategies, data-driven projects, with the aim of providing better urban services and interoperability based on metadata schemes and open-data formats, permanent access and data use and reuse, with the minimum possible legal, economic and technological barriers within current legislation.Fundamental valuesThe City of Virginia Beach’s data is a strategic asset and a valuable resource that enables our local government carry out its mission and its programs effectively. Appropriate access to municipal data significantly improves the value of the information and the return on the investment involved in generating it. In accordance with the Master Technology Plan 2.0 and its emphasis on public innovation, the digital economy and empowering city residents, this data-management strategy is based on the following considerations.Within this context, this new management and use of data has to respect and comply with the essential values applicable to data. For the Data and Information Team, these values are:Shared municipal knowledge. Municipal data, in its broadest sense, has a significant social dimension and provides the general public with past, present and future knowledge concerning the government, the city, society, the economy and the environment.The strategic value of data. The team must manage data as a strategic value, with an innovative vision, in order to turn it into an intellectual asset for the organization.Geared towards results. Municipal data is also a means of ensuring the administration’s accountability and transparency, for managing services and investments and for maintaining and improving the performance of the economy, wealth and the general public’s well-being.Data as a common asset. City residents and the common good have to be the central focus of the City of Virginia Beach’s plans and technological platforms. Data is a source of wealth that empowers people who have access to it. Making it possible for city residents to control the data, minimizing the digital gap and preventing discriminatory or unethical practices is the essence of municipal technological sovereignty.Transparency and interoperability. Public institutions must be open, transparent and responsible towards the general public. Promoting openness and interoperability, subject to technical and legal requirements, increases the efficiency of operations, reduces costs, improves services, supports needs and increases public access to valuable municipal information. In this way, it also promotes public participation in government.Reuse and open-source licenses. Making municipal information accessible, usable by everyone by default, without having to ask for prior permission, and analyzable by anyone who wishes to do so can foster entrepreneurship, social and digital innovation, jobs and excellence in scientific research, as well as improving the lives of Virginia Beach residents and making a significant contribution to the city’s stability and prosperity.Quality and security. The city government must take firm steps to ensure and maximize the quality, objectivity, usefulness, integrity and security of municipal information before disclosing it, and maintain processes to effectuate requests for amendments to the publicly-available information.Responsible organization. Adding value to the data and turning it into an asset, with the aim of promoting accountability and citizens’ rights, requires new actions, new integrated procedures, so that the new platforms can grow in an organic, transparent and cross-departmental way. A comprehensive governance strategy makes it possible to promote this revision and avoid redundancies, increased costs, inefficiency and bad practices.Care throughout the data’s life cycle. Paying attention to the management of municipal registers, from when they are created to when they are destroyed or preserved, is an essential part of data management and of promoting public responsibility. Being careful with the data throughout its life cycle combined with activities that ensure continued access to digital materials for as long as necessary, help with the analytic exploitation of the data, but also with the responsible protection of historic municipal government registers and safeguarding the economic and legal rights of the municipal government and the city’s residents.Privacy “by design”. Protecting privacy is of maximum importance. The Data and Information Management Team has to consider and protect individual and collective privacy during the data life cycle, systematically and verifiably, as specified in the general regulation for data protection.Security. Municipal information is a strategic asset subject to risks, and it has to be managed in such a way as to minimize those risks. This includes privacy, data protection, algorithmic discrimination and cybersecurity risks that must be specifically established, promoting ethical and responsible data architecture, techniques for improving privacy and evaluating the social effects. Although security and privacy are two separate, independent fields, they are closely related, and it is essential for the units to take a coordinated approach in order to identify and manage cybersecurity and risks to privacy with applicable requirements and standards.Open Source. It is obligatory for the Data and Information Management Team to maintain its Open Data- Open Source platform. The platform allows citizens to access open data from multiple cities in a central location, regional universities and colleges to foster continuous education, and aids in the development of data analytics skills for citizens. Continuing to uphold the Open Source platform with allow the City to continually offer citizens the ability to provide valuable input on the structure and availability of its data. Strategic areasIn order to deploy the strategy for the responsible and ethical use of data, the following areas of action have been established, which we will detail below, together with the actions and emblematic projects associated with them.In general, the strategy pivots on the following general principals, which form the basis for the strategic areas described in this section.Data sovereigntyOpen data and transparencyThe exchange and reuse of dataPolitical decision-making informed by dataThe life cycle of data and continual or permanent accessData GovernanceData quality and accessibility are crucial for meaningful data analysis, and must be ensured through the implementation of data governance. IT will establish a Data Governance Board, a collaborative organizational capability made up of the city’s data and analytics champions, who will work together to develop policies and practices to treat and use data as a strategic asset.Data governance is the overall management of the availability, usability, integrity and security of data used in the city. Increased data quality will positively impact overall trust in data, resulting in increased use and adoption. The ownership, accessibility, security, and quality, of the data is defined and maintained by the Data Governance Board.To improve operational efficiency, an enterprise-wide data catalog will be created to inventory data and track metadata from various data sources to allow for rapid data asset discovery. Through the data catalog, the city will

CER_SYN1deg-Month_Terra-Aqua-NOAA20_Edition4B is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-Aqua-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; CERES FM3, CERES FM4, CERES Scanner, and MODIS on Aqua; and CERES FM6 and VIIRS on NOAA-20. Not all platforms are available for any particular data month. Data collection for this product is ongoing.CERES Synoptic (SYN) 1 degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes in order to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, and surface fluxes and computed fluxes that have been adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS , VIIRS, and geostationary satellite cloud properties along with atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are made for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to more accurately model variability between CERES observations. To use GEO data to enhance diurnal sampling, several steps are involved. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. To ensure GEO and CERES TOA fluxes are consistent, a normalization technique is used. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.CERES is a key component of the Earth Observing System (EOS) program. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, protoflight model (PFM), was launched on November 27, 1997 as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts. , The proposed network is built starting from two distinct data sources:

the entire dataset dump from Semantic Scholar (with particular emphasis on the authors and papers datasets) the entire list of Italian faculty members as maintained by Cineca (under appointment by the Italian Ministry of University and Research).

By means of a custom name-identity recognition algorithm (details are available in the accompanying paper published in Scientific Data), the names of the authors in the Semantic Scholar dataset have been mapped against the names contained in the Cineca dataset and authors with no match (e.g., because of not being part of an Italian university) have been discarded. The remaining authors will compose the nodes of the network, which have been enriched with node-related (i.e., author-related) attributes. In order to build the network edges, we leveraged the papers dataset from Semantic Scholar: specifically, any two authors are said to be connected if there is at least one pap..., , # Data cleaning and enrichment through data integration: networking the Italian academia

https://doi.org/10.5061/dryad.wpzgmsbwj

Manuscript published in Scientific Data with DOI .

Description of the data and file structure

This repository contains two main data files:

• edge_data_AGG.csv, the full network in comma-separated edge list format (this file contains mainly temporal co-authorship information);
• Coauthorship_Network_AGG.graphml, the full network in GraphML format.Â

along with several supplementary data, listed below, useful only to build the network (i.e., for reproducibility only):

• University-City-match.xlsx, an Excel file that maps the name of a university against the city where its respective headquarter is located;
• Areas-SS-CINECA-match.xlsx, an Excel file that maps the research areas in Cineca against the research areas in Semantic Scholar.

Description of the main data files

The `Coauthorship_Networ...

The datadaemon extension for CKAN enables the loading of RDF (Resource Description Framework) data and its storage as a CKAN resource. This functionality allows users to integrate semantic web data directly into their CKAN catalogs, making it accessible and manageable alongside other datasets. By bridging the gap between RDF data and CKAN's data management capabilities, the extension streamlines the process of incorporating structured data into open data portals. Key Features: RDF Data Loading: Facilitates the upload and ingestion of RDF data into CKAN. Resource Creation: Automatically creates CKAN resource(s) based on the uploaded RDF data. Data Storage: Stores the loaded RDF data as a standard CKAN resource, making it discoverable and accessible through CKAN's standard APIs and user interface. Integration of Semantic Web Data: Allows for integration of semantic web data facilitating the addition of schema.org, DCAT or other standard vocabulary. Technical Integration: Due to the limited information provided in the README, the precise integration details with CKAN are unclear. However, it can be inferred that the extension likely leverages CKAN's plugin architecture to add new functionalities related to RDF data handling. This likely involves utilizing CKAN's API for resource creation and management. Benefits & Impact: The datadaemon extension allows users to consolidate RDF data within their CKAN instances. This means enriched data catalogs and more useful data management.

1. Targeted enrichment of conserved genomic regions (e.g., ultraconserved elements or UCEs) has emerged as a promising tool for inferring evolutionary history in many organismal groups. Because the UCE approach is still relatively new, much remains to be learned about how best to identify UCE loci and design baits to enrich them.

2. We test an updated UCE identification and bait design workflow for the insect order Hymenoptera, with a particular focus on ants. The new strategy augments a previous bait design for Hymenoptera by (a) changing the parameters by which conserved genomic regions are identified and retained, and (b) increasing the number of genomes used for locus identification and bait design. We perform in vitro validation of the approach in ants by synthesizing an ant-specific bait set that targets UCE loci and a set of “legacy” phylogenetic markers. Using this bait set, we generate new data for 84 taxa (16/17 ant subfamilies) and extract loci from an additional 17 genome-enabled taxa. We then use these data to examine UCE capture success and phylogenetic performance across ants. We also test the workability of extracting legacy markers from enriched samples and combining the data with published data sets.

3. The updated bait design (hym-v2) contained a total of 2,590-targeted UCE loci for Hymenoptera, significantly increasing the number of loci relative to the original bait set (hym-v1; 1,510 loci). Across 38 genome-enabled Hymenoptera and 84 enriched samples, experiments demonstrated a high and unbiased capture success rate, with the mean locus enrichment rate being 2,214 loci per sample. Phylogenomic analyses of ants produced a robust tree that included strong support for previously uncertain relationships. Complementing the UCE results, we successfully enriched legacy markers, combined the data with published Sanger data sets, and generated a comprehensive ant phylogeny containing 1,060 terminals.

4. Overall, the new UCE bait design strategy resulted in an enhanced bait set for genome-scale phylogenetics in ants and likely all of Hymenoptera. Our in vitro tests demonstrate the utility of the updated design workflow, providing evidence that this approach could be applied to any organismal group with available genomic information.

Unfortunately, no README file was found for the datano extension, limiting the ability to provide a detailed and comprehensive description. Therefore, the following description is based on the extension name and general assumptions about data annotation tools within the CKAN ecosystem. The datano extension for CKAN, presumably short for "data annotation," likely aims to enhance datasets with annotations, metadata enrichment, and quality control features directly within the CKAN environment. It potentially introduces functionalities for adding textual descriptions, classifications, or other forms of annotation to datasets to improve their discoverability, usability, and overall value. This extension could provide an interface for users to collaboratively annotate data, thereby enriching dataset descriptions and making the data more useful for various purposes. Key Features (Assumed): * Dataset Annotation Interface: Provides a user-friendly interface within CKAN for adding structured or unstructured annotations to datasets and associated resources. This allows for a richer understanding of the data's content, purpose, and usage. * Collaborative Annotation: Supports multiple users collaboratively annotating datasets, fostering knowledge sharing and collective understanding of the data. * Annotation Versioning: Maintains a history of annotations, enabling users to track changes and revert to previous versions if necessary. * Annotation Search: Allows users to search for datasets based on annotations, enabling quick discovery of relevant data based on specific criteria. * Metadata Enrichment: Integrates annotations with existing metadata, enhancing metadata schemas to support more detailed descriptions and contextual information. * Quality Control Features: Includes options to rate, validate, or flag annotations to ensure they are accurate and relevant, improving overall data quality. Use Cases (Assumed): 1. Data Discovery Improvement: Enables users to find specific datasets more easily by searching for datasets based on their annotations and enriched metadata. 2. Data Quality Enhancement: Allows data curators to improve the quality of datasets by adding annotations that clarify the data's meaning, provenance, and limitations. 3. Collaborative Data Projects: Facilitates collaborative data annotation efforts, wherein multiple users contribute to the enrichment of datasets with their knowledge and insights. Technical Integration (Assumed): The datano extension would likely integrate with CKAN's existing plugin framework, adding new UI elements for annotation management and search. It could leverage CKAN's API for programmatic access to annotations and utilize CKAN's security model for managing access permissions. Benefits & Impact (Assumed): By implementing the datano extension, CKAN users can leverage improvements to data discoverability, quality, and collaborative potential. The enhancement can help data curators to refine the understanding and management of data, making it easier to search, understand and promote data driven decision-making.

SQUAD - Smart Qualitative Data: Methods and Community Tools for Data Mark-Up is a demonstrator project that will explore methodological and technical solutions for exposing digital qualitative data to make them fully shareable, exploitable and archivable for the longer term. Such tools are required to exploit fully the potential of qualitative data for adventurous collaborative research using web-based and e-science systems. An example of the latter might be linking multiple data and information sources, such as text, statistics and maps. Initially, the project deals with specifying and testing flexible means of storing and marking-up, or annotating, qualitative data using universal standards and technologies, through eXtensible Mark-up Language (XML).A community standard, or schema, will be proposed that will be applicable to most kinds of qualitative data. The second strand investigates optimal requirements for describing or 'contextualising' research data (e.g. interview setting or interviewer characteristics), aiming to develop standards for data documentation. The third strand aims to use natural language processing technologies to develop and implement user-friendly tools for semi-automating processes to prepare marked-up qualitative data. Finally, the project will investigate tools for publishing the enriched data and contextual information to web-based systems and for exporting to preservation formats. Tools and technologies to explore new forms of sharing and disseminating qualitative data

One manifestation of individualization is a progressively differential response of individuals to the non-shared components of the same environment. Individualization has practical implications in the clinical setting, where subtle differences between patients are often decisive for the success of an intervention, yet there has been no suitable animal model to study its underlying biological mechanisms. Here we show that enriched environment (ENR) can serve as a model of brain individualization. We kept 40 isogenic female C57BL/6JRj mice for 3 months in ENR and compared these mice to an equally sized group of standard-housed control animals, looking at the effects on a wide range of phenotypes in terms of both means and variances. Although ENR influenced multiple parameters and restructured correlation patterns between them, it only increased differences among individuals in traits related to brain and behavior (adult hippocampal neurogenesis, motor cortex thickness, open field and object exploration), in agreement with the hypothesis of a specific activity-dependent development of brain individuality.

AI-Powered Knowledge Graph Market Outlook

According to our latest research, the global AI-Powered Knowledge Graph market size reached USD 2.45 billion in 2024, demonstrating a robust momentum driven by rising enterprise adoption of AI-driven data structuring tools. The market is expected to expand at a CAGR of 25.8% from 2025 to 2033, reaching a projected value of USD 19.1 billion by 2033. This significant growth is fueled by the increasing demand for advanced data integration, real-time analytics, and intelligent automation across diverse industry verticals. As per our latest research, the market’s acceleration is underpinned by a confluence of digital transformation initiatives, surging investments in AI infrastructure, and the growing need for contextual data insights to drive business decisions.

The primary growth factor propelling the AI-Powered Knowledge Graph market is the exponential rise in data generation and the urgent need for organizations to derive meaningful, actionable intelligence from vast, disparate data sources. Modern enterprises are inundated with both structured and unstructured data originating from internal systems, customer interactions, social media, IoT devices, and external databases. Traditional data management tools are increasingly inadequate for extracting context-rich insights at scale. AI-powered knowledge graphs leverage advanced machine learning and natural language processing to semantically link data points, enabling enterprises to create a holistic, interconnected view of their information landscape. This capability not only enhances data discoverability and accessibility but also supports intelligent automation, predictive analytics, and personalized customer experiences, all of which are critical for maintaining competitive advantage in today’s digital economy.

Another key driver for the AI-Powered Knowledge Graph market is the growing focus on digital transformation across sectors such as BFSI, healthcare, retail, and manufacturing. Organizations in these industries are under pressure to modernize their IT infrastructure, optimize operations, and deliver superior customer engagement. AI-powered knowledge graphs play a pivotal role in these transformation initiatives by breaking down data silos, enriching data with contextual meaning, and enabling seamless integration of information across platforms and business units. The ability to automate knowledge discovery and reasoning processes streamlines compliance, risk management, and decision-making, which is particularly valuable in highly regulated sectors. Furthermore, the adoption of cloud-based deployment models is accelerating, offering scalability, flexibility, and cost efficiencies that further stimulate market growth.

The proliferation of AI and machine learning technologies, coupled with rapid advancements in natural language understanding, has significantly expanded the capabilities and applications of knowledge graphs. Modern AI-powered knowledge graphs can ingest, process, and interlink data from a multitude of sources in real time, supporting advanced use cases such as fraud detection, recommendation engines, and information retrieval. The integration of AI enables knowledge graphs to evolve dynamically, learning from new data and user interactions to continuously improve accuracy and relevance. This adaptability is particularly valuable as organizations face ever-changing business environments and increasingly complex data ecosystems. As a result, the market is witnessing heightened interest from both large enterprises and SMEs seeking to harness the full potential of their data assets.

Regionally, North America continues to dominate the AI-Powered Knowledge Graph market, accounting for the largest revenue share in 2024, owing to the early adoption of AI technologies, strong presence of leading vendors, and significant investments in digital infrastructure. Europe follows closely, driven by stringent data regulations and a robust ecosystem of technology innovators. Meanwhile, the Asia Pacific region is experiencing the fastest growth, propelled by expanding digital economies, increasing cloud adoption, and supportive government initiatives. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a smaller base, as enterprises in these regions accelerate their digital transformation journeys. The global market’s trajectory is thus shaped by a combination of technological innovation, industry-specific requirements, and regional economic dynam

Ecosystems are connected by flows of nutrients and organisms. Changes to connectivity and nutrient enrichment may destabilise ecosystem dynamics far from the nutrient source. We used gradostats to examine the effects of trophic connectivity (movement of consumers and producers) versus nutrient-only connectivity on the dynamics of Daphnia pulex (consumers) and algae (resources) in two metaecosystem configurations (linear vs. dendritic). We found that Daphnia peak population size and instability (coefficient of variation; CV) increased as distance from the nutrient input increased, but these effects were lower in metaecosystems connected by all trophic levels compared to nutrient-only connected systems and/or in dendritic compared to linear systems. We examined the effects of trophic connectivity (i.e. both trophic levels move rather than one or the other) using a generic model to qualitatively assess whether the expectations align with the ecosystem dynamics we observed. Analysis of our model shows that increased Daphnia population sizes and fluctuations in consumer-resource dynamics are expected with nutrient connectivity, with this pattern being more pronounced in linear rather than dendritic systems. These results confirm that connectivity may propagate and even amplify instability over a metaecosystem to communities distant from the source disturbance, and suggest a direction for future experiments, that recreate conditions closer to those found in natural systems.

Methods Our gradostat flasks contained simple communities of the water flea Daphnia pulex consuming a mix of three algal species (Pseudokirchneriella subcapitata, Scenedesmus quadricauda, Ankistrodesmus falcatus). This experiment employed a 2x2x2 factorial design to test the importance of ecosystem trophic connectivity (a treatment considering movement of medium only vs. movement of media, phytoplankton and Daphnia between flasks) and metaecosystem configuration (linear or dendritic) on the stability of Daphnia populations and algal communities with two levels of enriched medium input (regular and phosphorus-enriched). Four replicates of this whole design were established, for a total of 32 metaecosystems, run in 9 blocks due to time and space constraints. Each metaecosystem consisted of four “nodes” of 500 mL Erlenmeyer flasks with a foam stopper to allow for gas exchange (128 flasks total), seeded initially with 100 mL algal mix (total average algal density of 2.22 x106 +/- 1.3x104 cells/mL) to which 50 adult Daphnia with eggs (which produce broods of about 15 individuals each week in good conditions (Schwartz 1984) were added before topping off the flask to 500 mL with FLAMES media (Celis-Salgado et al. 2008). Configuration was controlled by unidirectionally connecting flasks in either a linear configuration (in →1→2→3→4→out) or a dendritic configuration (in →1, in→2, 1→3, 2→3, 3→4→out). We chose this as the simplest possible design in which a linear network could be compared to a branched network, with four nodes being the smallest possible number of nodes to create a dendritic configuration, and the two nodes branching into a third, similar to headwater in a river. Flasks were then connected by Tygon tubing and from an inflow reservoir of FLAMES medium (10 μgP/L) or enriched P (70 μgP /L) medium which was pumped through the array of flasks using peristaltic pumps (Watson-Marlow 503S/RL and Rainin Dynamix RP-1). Pumps were set on automatic timers to run for one hour each day at a speed adjusted to move a specific volume of media over that hour. The dilution rate was 10% of the total volume per for all flasks in the linear configurations and the “hub” (3) and “terminal” (4) nodes of the dendritic configurations (50 mL), and 5% per day (25 mL) for the “upstream” nodes in the dendritic configurations (Figure 1). We also controlled functional connectivity, contrasting metaecosystem dynamics when only nutrients moved versus the case when nutrients, resources and consumers moved. To block the flow of organisms in the nutrient-only connectivity treatment, outflow tubing was placed inside an 80-µm nylon mesh held in place with the stopper. Due to colony formation of the phytoplankton and clogging of the mesh, this proved to be an effective retention mechanism also for the algal resources, thus we believe flow of algae was significantly reduced in these treatments compared the trophic connectivity treatments. Though it is possible a small portion of single cells were able to pass through, Scenedesmus is known to form four-cell colonies in the presence of consumers (which we also observed in our algal counts), which are too large to pass through the mesh. As D. pulex were unable to fit through the tubing or survive moving through the peristaltic pumps, in the trophic connectivity treatment, D. pulex were manually moved using a 2mL transfer pipette at a rate of 10% of the population per day (20% were moved after each sampling count as sampling was only done every two days) in all linear nodes and the hub and terminal dendritic nodes, and 5% per day (10% moved after sampling) in the upstream dendritic nodes, in the same downstream direction as media. This type of passive movement at the flow rate of the system would be typical of planktonic animals in rivers that cannot swim upstream. Inflow stock solutions were prepared using FLAMES media (10 μgP/L). Finally, we modified our inflow reservoirs to contain either additionally P-enriched (high P) or regular (low P) FLAMES media. To increase P in the additionally nutrient-enriched treatment without changing pH, 132 μg/L of H2KPO4 and 168 μg/L H2KPO4 were added to our increased P treatment inflow stock solution. For the less-phosphorus treatment, no additional phosphorus was added, but 218 μg/L KCl were added to control for the K added to the high-P medium. See Figure S1 for a photograph of the experimental setup. Experimental Sampling The gradostats were sampled every other day for 30 days. In each node, the concentration of each algal species was measured using a haemocytometer. To estimate Daphnia population size, a 2mL plastic transfer pipette was used to gently agitate, and then sample each node. The number of individuals and two age classes (adult or juvenile) in the pipette were determined and then replaced to the experimental flask. This process was repeated five times, and the average D. pulex count of the five samples was used to estimate Daphnia density/2mL (total number estimated per flask = sampled count average *250). A pilot experiment testing this method proved it had an average error of 17.41 %, equating to 2.5 Daphnia more or less than the expected count at known densities; there is no reason to believe this error was systematic in one direction or the other, or to be systematically biased among our treatments. On Day 30 of the experiment, 40mL samples were taken from each flask to be analysed for total phosphorus concentration (TP). Phosphorus samples were analysed using a standard protocol (Wetzel and Likens 2013) at the GRIL-Université du Québec à Montréal analytical laboratory. Statistical Analysis To quantify the instability of Daphnia populations in experimental gradostats, we determined the peak total Dapnhia population size (as estimated by our density samples) and the coefficient of variation (CV) of Daphnia population size over the course of the experiment. These variables were calculated for each node within each gradostat, as well as in aggregate summed across all nodes for additive Daphnia metapopulation peak and CV. Similarly, population CV and peak density were calculated for each species of alga but we analyse here values based on total algal community density (sum of all species present), as Pseudorkirchinella and Ankistrodesmus were undetectable in most flasks for most of the experiment. Scenedesmus was mostly observed in 4-cell colonies, which is common in the presence of consumers, but we counted the total number of cells, not colonies. All analyses of experimental gradostat data were conducted in R version 4 (Team 2020). Statistical tests of the hypothesis were two-sided and with a level of significance of α=0.05. To determine whether metaecosystem connectivity, configuration and nutrient enrichment, as well as node position (1 upstream to 4 terminal), influenced node Daphnia population instability downstream of the nutrient enrichment source, we analysed the effects of these factors on mean Daphnia population and algal community peak values, on mean Daphnia population and algal community CV log-transformed (natural logarithm) values, and on mean final TP concentrations values, using linear mixed-effects models with the four factors as fixed effects. The mixed model included a random effect for ‘system’ which allowed us to account for a possible clustering in the response variables since the four nodes were connected as metaecosystems. For each of these models, pairwise interactions between factors were tested and terms for non-significant interactions were removed from the final models we report. Assumptions on the model errors (randomness, normality, and homoscedasticity) and the presence of possible influential observations or outliers were assessed with diagnostic plots of the model residuals. Robust standard errors (Huang and Li 2022) were used to adjust for heteroscedasticity. We also measured Daphnia metapopulation and algal metacommunity instability at the scale of the entire metaecosystem. To determine whether metaecosystem connectivity, configuration and nutrient enrichment influenced Daphnia metapopulation and algal metacommunity instability, we analysed the effects of these factors on mean Daphnia metapopulation and algal metacommunity peak values, and on mean CV values, using linear mixed-effects models with the three factors as fixed effects, using the block in which a metaecosystem was run

Edible insects have been proposed as a novel and sustainable source of protein and other essential nutrients for human consumption but nutrient absorption efficiency is still uncertain. We investigated zinc absorption from house crickets (Acheta domesticus) in a single-center and single-blinded cross-over study with children aged 24-36 months old in Kenya from September-November 2021. For this, children were randomized to consume two different experimental meals labeled with stable isotopes of zinc (Zn) at two different days, separated by a wash-out period of one month. Primary endpoints were the differences in amount of absorbed zinc (AZ) from maize-based meals enriched with intrinsically 67Zn-labeled house crickets (2.61 mg Zn, n= 28) in comparison with meals enriched with 68Zn (low-enriched: 0.90 mg Zn, n= 29) ; high-enriched: 3.24 mg Zn, n= 28) or with intrinsically 67Zn-labeled low-chitin cricket flour (2.51 mg Zn, n= 25 ), whereas the secondary endpoints were the differences in fractional zinc absorption. We found that AZ from meals with whole crickets (geometric mean: 0.36 mg; 95%CI: 0.30, 0.43) was 2.6 times higher than from low-enriched maize meals (0.14 mg; 0.11, 0.16; P<0.001), while it was not different from low-chitin cricket flour meals. Absorbed zinc from both cricket meals was higher than that from high-enriched meals. No severe adverse side events were reported. We conclude that edible house crickets are a good source of well-absorbable zinc, and their increased consumption could contribute to the alleviation of zinc deficiency. This trial was registered at the Pan African Clinical Trials Registry as PACTR202104533831364.

List of 1655 genes with a negative jGRP statistic meaning a down-regulation in LUAD tissues relative to normal tissues on the three LUAD data sets. Table S2. List of 1626 genes with a positive jGRP statistic meaning a up-regulation in LUAD tissues relative to normal tissues on the three LUAD data sets. Table S3. List of 42 KEGG pathways significantly enriched in the DEG lists of jGRP (τ = 0.7) by DAVID. Table S4. List of 57 KEGG pathways significantly enriched in the DEG lists of Fisher’s by DAVID. Table S5. List of 53 KEGG pathways significantly enriched in the DEG lists of AW by DAVID. Table S6. List of 40 KEGG pathways significantly enriched in the DEG lists of RP by DAVID. Table S7. List of 20 KEGG pathways significantly enriched in the DEG lists of Pooled cor by DAVID. (RAR 259 kb)