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The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY

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Analysis of ‘🍕 Pizza restaurants and Pizzas on their Menus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pizza-restaurants-and-pizzas-on-their-menuse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

About this Data

This is a list of over 3,500 pizzas from multiple restaurants provided by Datafiniti's Business Database. The dataset includes the category, name, address, city, state, menu information, price range, and more for each pizza restaurant.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do with this Data

You can use this data to discover how much you can expect to pay for pizza across the country. E.g.:

• What are the least and most expensive cities for pizza?
• What is the number of restaurants serving pizza per capita (100,000 residents) across the U.S.?
• What is the median price of a large plain pizza across the U.S.?
• Which cities have the most restaurants serving pizza per capita (100,000 residents)?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset was created by Datafiniti and contains around 10000 samples along with Longitude, Price Range Max, technical information and other features such as: - Date Updated - Categories - and more.

How to use this dataset

• Analyze Date Added in relation to Province
• Study the influence of Price Range Min on Address
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Datafiniti

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

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The EVIDENT Discrete Choice Experiment seeks to explore the impact of energy related financial literacy, consumer motivation, point-of-sale information and demographic factors on discount rate and willingness to pay for efficient household appliances. Across a series of choice experiments, the impact of factors such as financial information (purchase price, operating cost, salience of financial information), risk reduction (i.e. extended warranty), and financial capacity (i.e. low cost loans) on implicit discount rates for home appliances is examined. Further, the impact of direct rebound rates on efficient appliance selection is examined.

The experiment consists of the following sections: 1) demographic information; 2) current home appliance purchasing behaviour; 3) financial literacy; 4) environmental literacy; 5) stated preference experiment consisting of four choice points; 6) discount rates; 7) discrete choice experiment consisting of ten choice points; and 8) questions examining direct rebound rates associated with the novel appliance selected.

As noted above, two choice experiments are included within the current use case. The first of these is a stated preference experiment which examines the impact of financial and energy framing on willingness-to-pay for energy efficient appliances. Four choice points are presented within this experiment. Choice 1 presents five identical versions of an appliance which differ only by key feature, and seeks to reduce hypothetical bias across the choice experiment. For example, for a washing machine the key features are cost, capacity, spin speed, quick wash time and pause wash functionality. Choice 2 consists of the participants initial choice (at choice 1) alongside alternatives which differ only in purchase price and energy rating, with purchase price greater for more efficient appliances (I.e. A rated appliances are most expensive; D rated appliances are least expensive). Choice 3 is similar to choice 2, however in this instance operational costs per month are also presented. Again, operational costs are lower for more efficient appliances. Choice 3 is similar to choice 3 however in this instance operational costs per year are presented.

The second choice experiment is the DCE which explores the relative impacts of risk reduction (extended warranty), and financial supports (low cost loan, loan term) on willingness to invest in more efficient energy appliances. Attributes were selected based on literature review, focus group analyses, cognitive walk-through and usability analyses. Once final attributes were determined, choice cards were developed using a fractional factorial design. A statistically efficient main-effects design with 10 choice sets was created in R studio using the idefix package. As such, participants are presented with a series of ten choice points, each consisting of two appliances and a ‘no preference’ option.

More information on the EVIDENT Discrete Choice Experiment can be found on the public deliverables of the EVIDENT project https://evident-h2020.eu/deliverables/. More specifically, the experiment's theoretical framework and motivation are described in deliverable D1.2 Assessing behavioural biases and financial literacy, in section 5 while the final design is reported in D2.2 Optimised Protocols Design

Analysis of ‘Boat Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/karthikbhandary2/boat-sales on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

You are working as a data analyst for a yacht and boat sales website. The marketing team is preparing a weekly newsletter for boat owners. The newsletter is designed to help sellers to get more views of their boat, as well as stay on top of market trends.

They would like me to take a look at the recent data and get some insights. The possible questions that we can ask ourselves is:

1. characteristics of the most viewed boat listings in the last 7 days
2. is it the most expensive boats that get the most views?
3. Are there common features among the most viewed boats?

--- Original source retains full ownership of the source dataset ---

Digital elevation models (DEMs) have been found to be an effective data source for automated mapping of wetlands. However, it is unclear whether high spatial resolution DEMs, which tend to be more expensive to acquire and process, are necessary for mapping wetlands such as those in the US National Wetland Inventory (NWI). Therefore, we compared predictions of the probability of palustrine wetland occurrence with a random forests (RF) algorithm using DEMs generated from light detection and ranging (LiDAR) at 1 m, 3 m, and 10 m raster cell sizes; and photogrammetrically-derived DEMs at 3 m and 10 m. For each classification, a wide range of terrain derivatives were generated and used as the input data for the classification. Comparisons between the wetland predictions were made using the receiver operating characteristic (ROC) area under the curve (AUC) measure, the Kappa statistic, overall accuracy, class user’s and producer’s accuracy, and the out of bag (OOB) error rate. For two different study sites, irrespective of the source of the digital terrain data, palustrine wetland occurrence was predicted with AUC values greater than 0.95, overall accuracies greater than 88%, Kappa greater than 0.77, and wetland user’s and producer’s accuracies above 0.85 when using a large training data set derived from the NWI or a small separate data set of non-NWI data derived from field samples. We therefore conclude that the source (LiDAR vs photogrammetric) and spatial scale (1 m, 3 m, or 10 m) of the DEM data does not have a large impact on the accuracy of the prediction of wetlands such as those in the NWI. However, for small wetlands, or more generally for wetlands unlike those in the NWI, finer scale data (e.g. 1 m) derived from LiDAR may be preferable.

The Hartree–Fock plus dispersion plus first-order correlation (HFDc(1)) method consists in augmenting the HF interaction energy by the correlation part of the first-order interaction energy and the second-order dispersion and exchange-dispersion energies. All of the augmentation terms are computed using the symmetry-adapted perturbation theory based on density functional theory description of monomers [SAPT(DFT)]; thus, HFDc(1) is a fully ab initio method. A partly empirical version of this method, HFDasc(1), uses a damped asymptotic expansion for the dispersion plus exchange-dispersion term fitted to SAPT(DFT) ab initio values. The HFDc(1) interaction energies for dimers in the S22, S66, S66x8, NCCE31, IonHB, and UD-ARL benchmark data sets are more accurate than those given by most ab initio methods with comparable costs. HFDc(1) can be used routinely for dimers with nearly 200 atoms, such as included in the S12L benchmark set, giving results comparable to those obtained by the most expensive methods applicable.

Introduction

Vessel segmentation in fundus images is essential in the diagnosis and prognosis of retinal diseases and the identification of image-based biomarkers. However, creating a vessel segmentation map can be a tedious and time consuming process, requiring careful delineation of the vasculature, which is especially hard for microcapillary plexi in fundus images. Optical coherence tomography angiography (OCT-A) is a relatively novel modality visualizing blood flow and microcapillary plexi not clearly observed in fundus photography. Unfortunately, current commercial OCT-A cameras have various limitations due to their complex optics making them more expensive, less portable, and with a reduced field of view (FOV) compared to fundus cameras. Moreover, the vast majority of population health data collection efforts do not include OCT-A data.

We believe that strategies able to map fundus images to en-face OCT-A can create precise vascular vessel segmentation with less effort.

In this dataset, called UTHealth - Fundus and Synthetic OCT-A Dataset (UT-FSOCTA), we include fundus images and en-face OCT-A images for 112 subjects. The two modalities have been manually aligned to allow for training of medical imaging machine learning pipelines. This dataset is accompanied by a manuscript that describes an approach to generate fundus vessel segmentations using OCT-A for training (Coronado et al., 2022). We refer to this approach as "Synthetic OCT-A".

Fundus Imaging

We include 45 degree macula centered fundus images that cover both macula and optic disc. All images were acquired using a OptoVue iVue fundus camera without pupil dilation.

The full images are available at the fov45/fundus directory. In addition, we extracted the FOVs corresponding to the en-face OCT-A images collected in cropped/fundus/disc and cropped/fundus/macula.

Enface OCT-A

We include the en-face OCT-A images of the superficial capillary plexus. All images were acquired using an OptoVue Avanti OCT camera with OCT-A reconstruction software (AngioVue). Low quality images with errors in the retina layer segmentations were not included.

En-face OCTA images are located in cropped/octa/disc and cropped/octa/macula. In addition, we include a denoised version of these images where only vessels are included. This has been performed automatically using the ROSE algorithm (Ma et al. 2021). These can be found in cropped/GT_OCT_net/noThresh and cropped/GT_OCT_net/Thresh, the former contains the probabilities of the ROSE algorithm the latter a binary map.

Synthetic OCT-A

We train a custom conditional generative adversarial network (cGAN) to map a fundus image to an en face OCT-A image. Our model consists of a generator synthesizing en face OCT-A images from corresponding areas in fundus photographs and a discriminator judging the resemblance of the synthesized images to the real en face OCT-A samples. This allows us to avoid the use of manual vessel segmentation maps altogether.

The full images are available at the fov45/synthetic_octa directory. Then, we extracted the FOVs corresponding to the en-face OCT-A images collected in cropped/synthetic_octa/disc and cropped/synthetic_octa/macula. In addition, we performed the same denoising ROSE algorithm (Ma et al. 2021) used for the original enface OCT-A images, the results are available in cropped/denoised_synthetic_octa/noThresh and cropped/denoised_synthetic_octa/Thresh, the former contains the probabilities of the ROSE algorithm the latter a binary map.

Other Fundus Vessel Segmentations Included

In this dataset, we have also included the output of two recent vessel segmentation algorithms trained on external datasets with manual vessel segmentations. SA-Unet (Li et. al, 2020) and IterNet (Guo et. al, 2021).

• SA-Unet. The full images are available at the fov45/SA_Unet directory. Then, we extracted the FOVs corresponding to the en-face OCT-A images collected in cropped/SA_Unet/disc and cropped/SA_Unet/macula.

• IterNet. The full images are available at the fov45/Iternet directory. Then, we extracted the FOVs corresponding to the en-face OCT-A images collected in cropped/Iternet/disc and cropped/Iternet/macula.

Train/Validation/Test Replication

In order to replicate or compare your model to the results of our paper, we report below the data split used.

• Training subjects IDs: 1 - 25

• Validation subjects IDs: 26 - 30

• Testing subjects IDs: 31 - 112

Data Acquisition

This dataset was acquired at the Texas Medical Center - Memorial Hermann Hospital in accordance with the guidelines from the Helsinki Declaration and it was approved by the UTHealth IRB with protocol HSC-MS-19-0352.

User Agreement

The UT-FSOCTA dataset is free to use for non-commercial scientific research only. In case of any publication the following paper needs to be cited

Coronado I, Pachade S, Trucco E, Abdelkhaleq R, Yan J, Salazar-Marioni S, Jagolino-Cole A, Bahrainian M, Channa R, Sheth SA, Giancardo L. Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks. Sci Rep 2023;13:15325. https://doi.org/10.1038/s41598-023-42062-9.

Funding

This work is supported by the Translational Research Institute for Space Health through NASA Cooperative Agreement NNX16AO69A.

Research Team and Acknowledgements

Here are the people behind this data acquisition effort:

Ivan Coronado, Samiksha Pachade, Rania Abdelkhaleq, Juntao Yan, Sergio Salazar-Marioni, Amanda Jagolino, Mozhdeh Bahrainian, Roomasa Channa, Sunil Sheth, Luca Giancardo

We would also like to acknowledge for their support: the Institute for Stroke and Cerebrovascular Diseases at UTHealth, the VAMPIRE team at University of Dundee, UK and Memorial Hermann Hospital System.

References

Coronado I, Pachade S, Trucco E, Abdelkhaleq R, Yan J, Salazar-Marioni S, Jagolino-Cole A, Bahrainian M, Channa R, Sheth SA, Giancardo L. Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks. Sci Rep 2023;13:15325. https://doi.org/10.1038/s41598-023-42062-9.


C. Guo, M. Szemenyei, Y. Yi, W. Wang, B. Chen, and C. Fan, "SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation," in 2020 25th International Conference on Pattern Recognition (ICPR), Jan. 2021, pp. 1236–1242. doi: 10.1109/ICPR48806.2021.9413346.

L. Li, M. Verma, Y. Nakashima, H. Nagahara, and R. Kawasaki, "IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks," 2020 IEEE Winter Conf. Appl. Comput. Vis. WACV, 2020, doi: 10.1109/WACV45572.2020.9093621.

Y. Ma et al., "ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model," IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 928–939, Mar. 2021, doi: 10.1109/TMI.2020.3042802.

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Here are a few use cases for this project:

1. Use Case "Stock Management": In retail environments, the "second-data" model can be utilized to monitor the stock levels of different drink brands, such as Cola, Fanta, and Sprite, in real-time. By identifying product on shelves, it helps in instant restocking based on demand, automates inventory management, and can also assist in setting up promotional campaigns for specific beverages.

2. Use Case "Recycling Initiatives": It can facilitate recycling initiatives by distinguishing between varying drink cans/bottles, supporting automated waste sorting, and ensuring each bottle/can is properly recycled based on its class. This can be particularly useful in waste management facilities or recycling schemes.

3. Use Case "Automated Vending Machines": Vending machines can incorporate this model to automatically identify and dispense the selected soda brand by visual recognition rather than traditional code-based systems.

4. Use Case "Consumer Behavior Research": Study consumer behavior in supermarkets or convenience stores. Using CCTV footage, the model can identify which soda brands consumers are interacting with the most, providing valuable insights for marketing and sales strategies.

5. Use Case "Autonomous Retail Checkout": This model can be implemented in autonomous checkout systems to identify the purchased soda brand. As customers add products to their cart, the system would identify them, adding the appropriate price to their running total, and making checkout seamless and quick.

The data was scraped from transfermarkt.com and pertains to the most valuable teams and players in the world of football.

First teams, In this dataset, data about the top 100 teams in the world is collected, and the data is collected based on Rank: team rank. Club: the name of the team. Competition: Name of the league in competition. Squad size refers to the number of team members. Ages: The average age of the players. Market Value: The market value of the team. Players' market value: Players' market worth. MV The top 18 players are as follows: A free market participant's values. MV share: the percentage of MV owned by the team.

Second Players, This dataset collects data on the top 100 players in the world, and the data is collected based on Rank: Player Rank. Name: The name of the player Position: Position of the player in the game Age: Player Age Matches: Total number of matches played Goals: The total number of goals scored. Assists: Total number of Assists scored Yellow_Cards: The total number of yellow cards issued this season. Red_Cards: The total number of red cards issued this season. Substitutions On: Total number for enter as Substitution Substitutions Offs: Total number for get out as Substitution

Recent breakthroughs in diffusion models, multimodal pretraining, and efficient finetuning have led to an explosion of text-to-image generative models. Given human evaluation is expensive and difficult to scale, automated methods are critical for evaluating the increasingly large number of new models. However, most current automated evaluation metrics like FID or CLIPScore only offer a holistic measure of image quality or image-text alignment, and are unsuited for fine-grained or instance-level analysis. In this paper, we introduce GenEval, an object-focused framework to evaluate compositional image properties such as object co-occurrence, position, count, and color. We show that current object detection models can be leveraged to evaluate text-to-image models on a variety of generation tasks with strong human agreement, and that other discriminative vision models can be linked to this pipeline to further verify properties like object color. We then evaluate several open-source text-to-image models and analyze their relative generative capabilities on our benchmark. We find that recent models demonstrate significant improvement on these tasks, though they are still lacking in complex capabilities such as spatial relations and attribute binding. Finally, we demonstrate how GenEval might be used to help discover existing failure modes, in order to inform development of the next generation of text-to-image models. Our code to run the GenEval framework is publicly available at this https URL.

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Nearshore areas within Long Island Sound are of great interest to the Connecticut and New York research and management communities because of their ecological, recreational, and commercial importance. However, although advances in multibeam echosounder technology permit the construction of detailed digital terrain models of seafloor topography within deeper waters, limitations inherent with collecting multibeam data make using this technology in shallower waters (<10-m deep) more difficult and expensive. These limitations have often resulted in gaps of no data between multibeam bathymetric datasets and the adjacent shoreline. To address this problem, complete-coverage multibeam bathymetry acquired offshore of New London and Niantic Bay, Connecticut, has been integrated with hydrographic LIDAR acquired along the nearshore. The result is a more continuous seafloor perspective and a much smaller gap between the digital bathymetric data and the shoreline. These datasets are provided as ESRI grid and GeoTIFF formats in order to facilitate access, compatibility, and utility.

A small dataset for beginners and someone who want to try basic anaylsis methods and something like that.

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What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

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