On May 3, 2011 Montgomery County passed legislation (Bill 8-11) that places a five-cent charge on each paper or plastic carryout bag provided by retail establishments in the County to customers at the point of sale, pickup or delivery. Retailers retain 1 cent of each 5 cents for the bags they sell a customer. This dataset represents information that has been captured since this law went into effect. Update Frequency - Monthly

ODDS Smart Building Depth Dataset

Introduction:

The goal of this dataset is to facilitate research focusing on recognizing objects in smart buildings using the depth sensor mounted at the ceiling. This dataset contains annotations of depth images for eight frequently seen object classes. The classes are: person, backpack, laptop, gun, phone, umbrella, cup, and box.

Data Collection:

We collected data from two settings. We had Kinect mounted at a 9.3 feet ceiling near to a 6 feet wide door. We also used a tripod with a horizontal extender holding the kinect at a similar height looking downwards. We asked about 20 volunteers to enter and exit a number of times each in different directions (3 times walking straight, 3 times walking towards left side, 3 times walking towards right side) holding objects in many different ways and poses underneath the Kinect. Each subject was using his/her own backpack, purse, laptop, etc. As a result, we considered varieties within the same object, e.g., for laptops, we considered Macbooks, HP laptops, Lenovo laptops of different years and models, and for backpacks, we considered backpacks, side bags, and purse of women. We asked the subjects to walk while holding it in many ways, e.g., for laptop, the laptop was fully open, partially closed, and fully closed while carried. Also, people hold laptops in front and side of their bodies, and underneath their elbow. The subjects carried their backpacks in their back, in their side at different levels from foot to shoulder. We wanted to collect data with real guns. However, bringing real guns to the office is prohibited. So, we obtained a few nerf guns and the subjects were carrying these guns pointing it to front, side, up, and down while walking.

Annotated Data Description:

The Annotated dataset is created following the structure of Pascal VOC devkit, so that the data preparation becomes simple and it can be used quickly with different with object detection libraries that are friendly to Pascal VOC style annotations (e.g. Faster-RCNN, YOLO, SSD). The annotated data consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the eight classes present in the image. Multiple objects from multiple classes may be present in the same image. The dataset has 3 main directories:

1)DepthImages: Contains all the images of training set and validation set.

2)Annotations: Contains one xml file per image file, (e.g., 1.xml for image file 1.png). The xml file includes the bounding box annotations for all objects in the corresponding image.

3)ImagesSets: Contains two text files training_samples.txt and testing_samples.txt. The training_samples.txt file has the name of images used in training and the testing_samples.txt has the name of images used for testing. (We randomly choose 80%, 20% split)

UnAnnotated Data Description:

The un-annotated data consists of several set of depth images. No ground-truth annotation is available for these images yet. These un-annotated sets contain several challenging scenarios and no data has been collected from this office during annotated dataset construction. Hence, it will provide a way to test generalization performance of the algorithm.

Citation:

If you use ODDS Smart Building dataset in your work, please cite the following reference in any publications: @inproceedings{mithun2018odds, title={ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs}, author={Niluthpol Chowdhury Mithun and Sirajum Munir and Karen Guo and Charles Shelton}, booktitle={ ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN)}, year={2018}, }

This visualization product displays plastic bags density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density of plastic bags (number of items per km²) = ?Number of plastic bags related items / Swept area (km²) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. The list of selected items for this product is attached to this metadata. Information on data processing and calculation is detailed in the attached methodology document. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This timeline presents Nike's North American revenue from 2009 to 2024, by segment. Nike's North American revenue from footwear amounted to roughly 14.5 billion U.S. dollars in the year ended May 31, 2024, which was far greater than that of the apparel and equipment segments combined. That said, only the equipment segment recorded noticeable sales growth in the last year. A broad portfolio Nike offers an extremely broad array of products within the apparel and sports equipment market. As one of the leading companies, Nike tries to stay ahead of the game and create new, unique and innovative products to give their athletes, and their profit margins the edge. This can be seen in the number of patents filed by Nike. These patents cover a wide array of technology areas, primarily design, followed by footwear. Nike shoes And Nike’s investment in footwear is rewarded, as the revenue of Nike’s footwear segment compared to Adidas and Puma is far greater than that of its competitors. Nike has many lines of iconic shoes, from their air Jordan line to the extremely limited-edition Nike Air mags. These were shoes based on the film Back to the Future, which feature self-lacing technology.

This is an open source - publicly available dataset which can be found at https://shahariarrabby.github.io/ekush/ . We split the dataset into three sets - train, validation, and test. For our experiments, we created two other versions of the dataset. We have applied 10-fold cross validation on the train set and created ten folds. We also created ten bags of datasets using bootstrap aggregating method on the train and validation sets. Lastly, we created another dataset using pre-trained ResNet50 model as feature extractor. On the features extracted by ResNet50 we have applied PCA and created a tabilar dataset containing 80 features. pca_features.csv is the train set and pca_test_features.csv is the test set. Fold.tar.gz contains the ten folds of images described above. Those folds are also been compressed. Similarly, Bagging.tar.gz contains the ten compressed bags of images. The original train, validation, and test sets are in Train.tar.gz, Validation.tar.gz, and Test.tar.gz, respectively. The compression has been performed for speeding up the upload and download purpose and mostly for the sake of convenience. If anyone has any question about how the datasets are organized please feel free to ask me at shiblygnr@gmail.com .I will get back to you in earliest time possible.

This dataset represents the GPS coordinates and other relevant information of where backpack application/treatment took place for mosquitoes for 2020.

A brief description of how it works:

GPS coordinates are obtained from a cell phone, which is connected to an Arduino. A stretch sensor connected to the Arduino is used to detect where pesticide is sprayed.

Things to consider when working with this dataset:

The setup has been tested for accuracy but it is possible for equipment to report incorrect data. Values may be changed due to correction or re-calibration. Values may also be incorrect due to malfunctioning equipment that went undetected for a period of time.

This timeline shows adidas Group's net sales worldwide from 2000 to 2024. In 2024, adidas Group's net sales amounted to about 23.7 billion euros. adidas The adidas Group is the largest sporting goods manufacturer in Europe and the second-largest worldwide – only behind long-term competitor Nike. The company manufactures sports clothing, as well as other products, such as bags, shirts, watches, eyewear, and so on. The company is headquartered in Herzogenaurach, Germany, but has many major locations all over the world. Adidas was founded in 1949 and employs nearly 60,000 employees worldwide. The company's name originated from its founder: ‘Adi’ from Adolf and ‘Das’ from Dassler’. The official adidas logo is characterized by the three stripes and the three leaves symbolizing the Olympic spirit, which combines the three continental plates. The adidas brand is one of the most valuable German sports brands and enjoys a great brand loyalty among consumers of both genders. The company's sales are divided among the following main product categories: footwear, apparel, and hardware. Footwear captured the largest sales share in 2024, as well as previous years. Besides selling sporting goods, the adidas Group has repeatedly sponsored a range of sport clubs involved in a large variety of different sports. However, the marketing focus of their sponsorship still focuses on the team sport of soccer.

This visualization product displays the plastic bags abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Selection of plastic bags related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Exclusion of surveys without associated length; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of plastic bags related items of the survey (normalized by 100 m) = Number of plastic bags related items of the survey x (100 / survey length) Then, this normalized number of plastic bags related items is summed to obtain the total normalized number of plastic bags related items for each survey. Finally, the median abundance of plastic bags related items for each beach and year is calculated from these normalized abundances of plastic bags related items per survey. Percentiles 50, 75, 95 & 99 have been calculated taking into account plastic bags related items from other sources data for all years. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.