This dataset explores the intersection of sustainable visual communication and design, utilizing big data and artificial intelligence. It includes data related to the visual communication of artistic graphics, with features such as color, regular combinations, and element arrangements. The dataset incorporates environmental metrics, material efficiency, user feedback, cultural relevance, and engagement rates, providing a comprehensive foundation for research into creating sustainable designs. The target variable, sustainability_score, is a weighted sum of various features, including energy consumption, environmental impact, and material efficiency, aimed at evaluating the sustainability of the visual design.

It is ideal for machine learning models focused on optimizing design processes, improving sustainability in visual communication, and exploring AI-driven approaches in design systems. The data is collected from social media platforms, websites, and surveys to provide valuable insights for both artistic and environmental optimization.

This dataset features over 330,000 high-quality interior design images sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a richly varied and extensively annotated collection of indoor environment visuals.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with object and scene detection metadata, making it ideal for tasks such as room classification, furniture detection, and spatial layout analysis. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities: the images are collected through a proprietary gamified platform for photographers. Competitions centered on interior design themes ensure a steady stream of fresh, high-quality submissions. Custom datasets can be sourced on-demand within 72 hours to fulfill specific requests, such as particular room types, design styles, or furnishings.

2. Global Diversity: photographs have been sourced from contributors in over 100 countries, covering a wide spectrum of architectural styles, cultural aesthetics, and functional spaces. The images include homes, offices, restaurants, studios, and public interiors—ranging from minimalist and modern to classic and eclectic designs.

3. High-Quality Imagery: the dataset includes standard to ultra-high-definition images that capture fine interior details. Both professionally staged and candid real-life spaces are included, offering versatility for training AI across design evaluation, object detection, and environmental understanding.

4. Popularity Scores: each image is assigned a popularity score based on its performance in GuruShots competitions. This provides valuable insights into global aesthetic trends, helping AI models learn user preferences, design appeal, and stylistic relevance.

5. AI-Ready Design: the dataset is optimized for machine learning tasks such as interior scene recognition, style transfer, virtual staging, and layout generation. It integrates smoothly with popular AI development environments and tools.

6. Licensing & Compliance: the dataset fully complies with data privacy regulations and includes transparent licensing suitable for commercial and academic use.

Use Cases: 1. Training AI for interior design recommendation engines and virtual staging tools. 2. Enhancing smart home applications and spatial recognition systems. 3. Powering AR/VR platforms for virtual tours, furniture placement, and room redesign. 4. Supporting architectural visualization, decor style transfer, and real estate marketing.

This dataset offers a comprehensive, high-quality resource tailored for AI-driven innovation in design, real estate, and spatial computing. Customizations are available upon request. Contact us to learn more!

Culturally oriented product design relies on inspiration from the local cultural heritage in the creation of unique products with specific local features. An authentic experience of cultural design inspiration can facilitate novel design outcomes. However, only a few studies have investigated the acquisition of cultural inspiration from a participatory perspective in the field. To narrow this gap, a design workshop was organized with local government in China. Design students were asked to combine local cultural characteristics in everyday products and to generate new concepts that reflect cultural diversity and support local tourism development. We collected students’ visual representations, text notes and recorded verbal explanations of the concepts behind the created product designs. The entire data was analysed following the method of holistic coding to identify the types of cultural inspiration and cultural levels. Datadriven analysis included two rounds of categorising. Using the product metaphorical mapping tool, we specified three cultural levels and the cultural elements related to them. The analytical method helped reveal students’ design intentions in applying both tangible and intangible cultural elements. The results demonstrated that design educators can support young designers to apply the participatory approach in bringing ethical cultural transformations regarding visual, behavioural and philosophical design features.

Digital health interventions (DHIs) hold promise for improving the reach of mental health care for adolescents, particularly those from under-resourced communities who may face significant barriers to accessing in-person care. Yet, low engagement and uptake have challenged DHIs’ potency. Human-centered design (HCD) integrates end-users (i.e., future users of the DHI) into iterative design processes, thereby prioritizing their needs and preferences. Clinical scientists are increasingly embracing HCD, but often lack expertise in how to apply these methods in practice. We provide a template for creating a design session interview guide in a needs assessment, which is the first phase in our HCD process to design a DHI for dysregulated eating in adolescents. To create the guide, we first conducted a “needs assessment” within our team to identify important topic areas that required feedback from adolescents (“investigate”). We then consolidated these ideas into structured domains through a brainstorming process (“ideate”), which resulted in an initial draft of a design session guide (“prototype”). Next, we piloted the prototype with members of our team and a technology-savvy adolescent (“evaluate”) to refine it prior to administration with the target audience (“refine and develop”). Our internal needs assessment identified that we needed to learn adolescents’ preferences for technology (e.g., desired features), clinical content (e.g., areas for specialized support), delivery (e.g., coaching), and developmental relevance (e.g., focus on self-regulation). We organized these topics into six domains: dysregulated eating experiences and current help-seeking behaviors, major challenges that impact dysregulated eating, preferred intervention features and skills, preferences for coaching support, the potential role of sensors to assess activity behaviors, and preferred aesthetics and brand. We created relevant prompts within each domain, revised, and reordered them to elicit more comprehensive responses during administration. Next, we practiced administering the guide internally amongst our team, then with a non-participant adolescent volunteer. Using HCD, we created a semi-structured design session interview guide that will be administered in an upcoming needs assessment with adolescents and will continue to evolve as we learn from adolescents. This case example unpacks the process of creating and iterating a design session guide that could be applied across clinical domains.

Datasets of n=1 Ruddlesden-Popper oxide chemistry with features

Data is designed to be used in Solid works or other common digital human modeling software systems. Data is used to model spatial consumption in determination of accessible seating area designs for passenger rail cars

Semi-structured interviews with Gig Workers in the USA were conducted remotely over a three-year period (2020-2022). Participants were recruited through in-person and online, and worked for a variety of task-based, delivery, and ride-hail companies that organize labor through a digital platform. Interview participants were recruited through a survey posted to Craigslist, Facebook groups, and Reddit forums for gig workers by platform. The research team acquired permission from forum moderators prior to posting. Survey respondents were contacted by email or text message to schedule an interview and were sent an informed consent document. The consent document included contact information for the PI and the University of Washington Institutional Review Board. Consent and permission to record were established verbally at the start of each interview. In the second phase of data collection, participants from the 2020 interviews and participants from focus groups conducted by the research team in 2019 were re-recruited over email for follow-up interviews. Eligible participants had worked on an app or platform within the last year, and were over the age of 18. Anonymized data include transcripts of interviews, data analysis protocols, and the questionnaire used to guide interviews. Complete study data available upon request (this repository only includes a sample of participant interviews). Note that you must be a registered and credentialed user of Dataverse to access this data.

The EGFE dataset is a collection of high-quality UI design prototypes with fragmented layered data. It includes high-fidelity UI screenshots and JSON files containing metadata of the design prototypes. This dataset aims to assist in merging fragmented elements within design prototypes, thereby alleviating the burden on developers to understand the designer's intent and aiding automated code generation tools in producing high-quality frontend code. What sets this dataset apart from others is the methodology employed to obtain UI screenshots and the hierarchical structure of views, which involves parsing UI design prototypes created using design tools like Sketch and Figma. It's important to note that a significant portion of the UI design drafts used in this dataset was generously provided by Alibaba Group, and their usage requires consent from Alibaba Group. To facilitate model testing, we have released a partial dataset here, adhering to the terms of the MIT license. If you require access to the complete dataset, please contact the authors of the paper.

In this repo, we release a total of 300 samples and pre-trained model checkpoints. It includes the following:

• EGFE-dataset.zip
  • xxx-assets.png: image of stacked elements
  • xxx.json: properties of UI elements
  • xxx.png: screenshot of high-fidelity UI design prototypes
• pre-trained model.7z
  • model checkpoints
  • tensorboard logs for visualization
  • a log.txt file
• design-prototype.7z
  • two design prototypes for testing data processing
• baseline model.7z
  • efficient_net: model config, checkpoints, and logs.
  • vit: model config, checkpoints, and logs.
  • swin: model config, checkpoints, and logs.
  • UIED_Classifier: Classifier checkpoints for UIED trained using the EGFE dataset (binary classification, i.e., components to be merged and components not to be merged).
  • UILM: model config, checkpoints, and logs.
  • ULDGNN: model config, checkpoints, and logs.

One motivation for a central bank digital currency (CBDC) is financial inclusion—bringing unbanked Americans into the payments system. To meet this goal, a CBDC would have to be designed to meet the specific needs of the diverse unbanked population.

This dataset contains the supplementary materials supporting the findings presented in the thesis, as well the peer-reviewed publications that have resulted from the doctoral research. Its structure mirrors that of the manuscript with one folder per Chapter, each including a subfolder for the raw materials, and another subfolder for the papers.

This exploratory study explores the potential contexts and opportunities for emerging visual data in urban forest design. Forestry experts deploy drone-mounted digital sensors to capture detailed visual and spatial data urban vegetation. These sensors generate point clouds that not only inform ecological analysis but also visually construct urban environments from a pedestrian perspective. Even though many data sources and visualisation tools such as GIS are available, how visualised data should be integrated into design practice is still unclear. Using a prototype multi-sourced data visualisation, we conducted eight semi-structured interviews with urban forestry experts to elicit reflections of the analytical and cultural roles of data visualisations in the domain. Thematic analysis of the interview transcripts revealed three design-oriented themes: (1) design analysis, (2) public engagement, and (3) sustainability. By analysing expert reflections, this paper considers potential research directions for visualising social and ecological data as a design material in the built environment. We discuss the implications of such visualisations for the broader community of spatial planning research including urban designers and communication scholars, proposing future research directions that leverage visual data to better design evolving urban landscapes.

This dataset contains data collected during a study "Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).

This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.

It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.

Purpose of the expert assessment The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.

Methodology Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching. To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (Lněnička & Nikiforova, 2021)*. The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point. Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in Lněnička et al. (2021)* is expected to be met. When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers. *Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605 *Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.

Test procedure (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.

Description of the data in this data set Sheet#1 "comparison_overall" provides results by portal Sheet#2 "comparison_category" provides results by portal and category Sheet#3 "category_subcategory" provides list of categories and its elements

Format of the file .xls

Licenses or restrictions CC-BY

For more info, see README.txt

Description This qualitative dataset is the second part of a PhD study on co-designing smart home technologies, and represents the data collected during a series of two in-person workshops: one with professionals developing smart technology and its early-adopters, and a second one with late/non-adopters of smart technology. The first workshop had four groups of participants and the second three groups. The data is divided by each group. The data collected during the previous and subsequent parts of the referred study are also available at Zenodo. Documents from workshop with professionals and early-adopters P2_WSP-PA-G1-TRANSCR_R00.docx (transcription of group 1 audio recordings) P2_WSP-PA-G1-VIS_000 to _005 (participant-generated visual data) P2_WSP-PA-G2-TRANSCR_R00.docx (transcription of group 2 audio recordings) P2_WSP-PA-G2-VIS_000 to _008 (participant-generated visual data) P2_WSP-PA-G3-TRANSCR_R00.docx (transcription of group 3 audio recordings) P2_WSP-PA-G3-VIS_000 to _004 (participant-generated visual data) P2_WSP-PA-G4-TRANSCR_R00.docx (transcription of group 4 audio recordings) P2_WSP-PA-G4-VIS_000 to _002 (participant-generated visual data) Documents from workshop with late/non-adopters P2_WSP-LN-G1-TRANSCR_R00 (transcription of group 1 audio recordings) P2_WSP-LN-G1-VIS_000 and _001 (participant-generated visual data) P2_WSP-LN-G2-TRANSCR_R00 (transcription of group 2 audio recordings) P2_WSP-LN-G2-VIS_000 to _003 (participant-generated visual data) P2_WSP-LN-G3-TRANSCR_R00 (transcription of group 3 audio recordings) P2_WSP-LN-G3-VIS_000 to _002 (participant-generated visual data) Acknowledgements This study is part of the GECKO Project (https://gecko-project.eu/) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (https://cordis.europa.eu/project/id/955422).

This is the 2024 Design Industry Statistics (as of 2023) provided by the Korea Institute of Design Promotion. This data represents the results of a survey conducted by the Ministry of Trade, Industry and Energy across the country, targeting general businesses, design-utilizing companies, design-specialized companies, central government agencies, and local governments. The survey was conducted to understand the current state of the design industry and inform policymaking. It serves as objective and reliable baseline data for design policy and strategy development across the government, industry, and academia, and is also useful for design research, business planning, and market analysis. It provides a comprehensive report (PDF) and detailed statistical tables covering the current state of design utilization, the extent of AI technology use in design work, the status of design development considering environmental factors, design investment and sales statistics, the size of the design workforce and the number of freelancers, and the overall market size and growth rate of the domestic design industry.

Find details of Craftsmen By Design Llc Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

Find details of European Cabinets By Design Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

This study is part of an ongoing Ph.D. research in the Department of Architectural Engineering + Technology, Faculty of Architecture and the Built Environment, Delft University of Technology (TU Delft), the Netherlands. The study aimed to develop strategies for design teams to facilitate the early-stage design and evaluationof building façades integrating solar cooling technologies. The strategies were developed using a research-through-design methodology, considering the Spanishcontext and a proposed evaluation set-up to assess techno-economic feasibility. The development of strategies involved mapping the design and evaluation of solar cooling integrated façades by identifying and relating key processes, inputs, outputs, design decisions, and tools within key design stages. The data are organized based on the following main phase followed in this case study:• Phase A: Energy simulation using DesignBuilder (DB) software.• Phase B: Solar Fraction (SF) Calculations• Phase C: Life-Cycle Cost (LCC) and Levelized Cost of Cooling (LCOC) Calculations• Phase D: Summarization of Scores

These data are in support of report DS 456 (Arnold and others, 2009). 30 equal-area polygons generated using techniques described in Scott (1990). Polygons include areas overlying the High Plains Aquifer in Colorado having a depth to water less than 180 feet, a saturated thickness greater than 50 feet, and underlying irrigated agricultural lands. Described in Arnold and others (2009). Input saturated thickness and depth to water data from V.L. McGuire, written communication, 2008. Irrigated agricultural lands from Bauder and others (2004).

Privacy-By-Design Analytics Market Outlook

According to our latest research, the global Privacy-By-Design Analytics market size reached USD 2.35 billion in 2024. The market is exhibiting robust momentum, propelled by a compound annual growth rate (CAGR) of 22.1% from 2025 to 2033. By 2033, the market is forecasted to reach USD 16.82 billion, underscoring the transformative impact of privacy regulations and enterprise digitalization. This growth is primarily driven by the increasing stringency of data protection laws, heightened awareness of consumer privacy, and the rapid adoption of advanced analytics solutions across various industries.

One of the most significant growth drivers for the Privacy-By-Design Analytics market is the intensification of global data privacy regulations. Regulations such as the European Union’s General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and similar frameworks in Asia Pacific and Latin America have compelled organizations to integrate privacy into the core of their analytics operations. These legal mandates are not only increasing compliance costs but also creating a compelling need for analytics solutions that can ensure privacy by default. Enterprises are now prioritizing privacy-centric analytics platforms that offer robust data governance, automated risk management, and real-time compliance monitoring, thereby fueling market expansion.

Another critical factor propelling the market is the exponential increase in data generation and the growing complexity of data ecosystems. As organizations across sectors such as BFSI, healthcare, IT, and retail digitize their operations, the volume and sensitivity of personal data being processed have skyrocketed. This surge necessitates advanced analytics solutions that can safeguard sensitive information throughout its lifecycle, from collection to analysis and storage. Privacy-by-design principles embedded into analytics platforms enable organizations to minimize data exposure, reduce the risk of breaches, and maintain consumer trust, which is becoming a significant differentiator in today’s data-driven economy.

Furthermore, the adoption of cloud-based analytics and the proliferation of remote work environments have introduced new privacy challenges and opportunities. Cloud deployment models are gaining traction due to their scalability, flexibility, and cost-effectiveness, but they also introduce complex privacy risks related to data residency, cross-border transfers, and third-party access. This dynamic is prompting organizations to invest in privacy-by-design analytics solutions that can seamlessly integrate with cloud infrastructures while ensuring compliance with multiple jurisdictional requirements. As a result, vendors are innovating rapidly, offering solutions with built-in privacy controls, encryption, and automated compliance features to address the evolving needs of a distributed workforce.

Regionally, North America continues to dominate the Privacy-By-Design Analytics market due to its early adoption of data privacy regulations and the presence of major technology vendors. However, Europe is rapidly catching up, driven by the enforcement of GDPR and increasing investment in privacy-centric technologies. Asia Pacific is emerging as a high-growth region, with countries like India, China, and Japan implementing stricter data protection laws and experiencing a surge in digital transformation initiatives. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slower pace, as regulatory landscapes mature and awareness of privacy risks grows among enterprises and consumers alike.

Component Analysis

The Component segment of the Privacy-By-Design Analytics market is bifurcated into software and services, each playing a pivotal role in the ecosystem. Software solutions dominate the market, accounting for a substantial share owing to the need for integrated platforms that offer end-to-end privacy management. These platforms often encompass modules for data discovery, risk assessment, compliance automation, and secure data analytics, enabling organizations to operationalize privacy across their analytics workflows. The software segment’s growth is further fueled by advancements in artificial intelligence and machine learning, which empower organizations to detect and mitigate privacy risks proactively.